Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

Structure and Ligand Based Virtual Screening and MPO Topological Analysis of Triazolo Thiadiazepine-fused Coumarin Derivatives as Anti-Parkinson Drug Candidates

Parkinson's disease (PD) is a debilitating condition that can cause locomotor problems in affected patients, such as tremors and body rigidity. PD therapy often includes the use of monoamine oxidase B (MAOB) inhibitors, particularly phenylhalogen compounds and coumarin-based semi-synthetic compounds. The objective of this study was to analyze the structural, pharmacokinetic, and pharmacodynamic profile of a series of Triazolo Thiadiazepine-fused Coumarin Derivatives (TDCDs) against MAOB, in comparison with the inhibitor safinamide. To achieve this goal, we utilized structure-based virtual screening techniques, including target prediction and absorption, distribution, metabolism, and excretion (ADME) prediction based on multi-parameter optimization (MPO) topological analysis, as well as ligand-based virtual screening techniques, such as docking and molecular dynamics. The findings indicate that the TDCDs exhibit structural similarity to other bioactive compounds containing coumarin and MAOB-binding azoles, which are present in the ChEMBL database. The topological analyses suggest that TDCD3 has the best ADME profile, particularly due to the alignment between low lipophilicity and high polarity. The coumarin and triazole portions make a strong contribution to this profile, resulting in a permeability with Papp estimated at 2.15 × 10-5 cm/s, indicating high cell viability. The substance is predicted to be metabolically stable. It is important to note that this is an objective evaluation based on the available data. Molecular docking simulations showed that the ligand has an affinity energy of - 8.075 kcal/mol with MAOB and interacts with biological substrate residues such as Pro102 and Phe103. The results suggest that the compound has a safe profile in relation to the MAOB model, making it a promising active ingredient for the treatment of PD.

Steroidal epoxides as anticancer agents in lung, prostate and breast cancers: The case of 1,2-epoxysteroids

Cancer continues to be a serious threat to human health worldwide. Lung, prostate and triple-negative breast cancers are amongst the most incident and deadliest cancers. Steroidal compounds are one of the most diversified therapeutic classes of compounds and they were proven to be efficient against several types of cancer. The epoxide function has been frequently associated with anticancer activity, particularly the 1,2-epoxide function. For this reason, three 1,2-epoxysteroid derivatives previously synthesised (EP1, EP2 and EP3) and one synthesised for the first time (oxysteride) were evaluated against H1299 (lung), PC3 (prostate) and HCC1806 (triple-negative breast) cancer cell lines. A human non-tumour cell line, MRC-5 (normal lung cell line) was also used. EP2 was the most active compound in all cell lines with IC50 values of 2.50, 3.67 and 1.95 µM, followed by EP3 with IC50 values of 12.65, 15.10 and 14.16 µM in H1299, PC3 and HCC1806 cells, respectively. Additional studies demonstrated that EP2 and EP3 induced cell death by apoptosis at lower doses and apoptosis/necrosis at higher doses, proving that their effects were dose-dependent. Both compounds also exerted their cytotoxicity by ROS production and by inducing double-strand breaks. Furthermore, EP2 and EP3 proved to be much less toxic against a normal lung cell line, MRC5, indicating that both compounds might be selective, and they also demonstrated suitable in silico ADME and toxicity parameters. Finally, none of the compounds induced haemoglobin release. Altogether, these results point out the extreme relevance of both compounds, especially EP2, in the potential treatment of these types of cancer.

Poisson-Boltzmann-based machine learning model for electrostatic analysis

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity, and geometric complexity associated with the PB equation. The present work introduces a PB-based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost.

Potentiating-antibiotic activity and absorption, distribution, metabolism, excretion and toxicity properties (ADMET) analysis of synthetic thiadiazines against multi-drug resistant (MDR) strains

BACKGROUND

Thiadiazines are heterocyclic compounds that contain two nitrogen atoms and one sulfur atom in their structure. These synthetic molecules have several relevant pharmacological activities, such as antifungal, antibacterial, and antiparasitic.

OBJECTIVES

The present study aimed to evaluate the possible in vitro and in silico interactions of compounds derived from thiadiazines.

METHODS

The compounds were initially synthesized, purified, and confirmed through HPLC methodology. Multi-drug resistant bacterial strains of Staphylococcus aureus 10 and Pseudomonas aeruginosa 24 were used to evaluate the direct and modifying antibiotic activity of thiadiazine derivatives. ADMET assays (absorption, distribution, metabolism, excretion, and toxicity) were conducted, which evaluated the influence of the compounds against thousands of macromolecules considered as bioactive targets.

RESULTS

There were modifications in the chemical synthesis in carbon 4 or 3 in one of the aromatic rings of the structure where different ions were added, ensuring a variability of products. It was possible to observe results that indicate the possibility of these compounds acting through the cyclooxygenase 2 mechanism, which, in addition to being involved in inflammatory responses, also acts by helping sodium reabsorption. The amine group present in thiadiazine analogs confers hydrophilic characteristics to the substances, but this primary characteristic has been altered due to alterations and insertions of other ligands. The characteristics of the analogs generally allow easy intestinal absorption, reduce possible hepatic toxic effects, and enable possible neurological and anti-inflammatory action. The antibacterial activity tests showed a slight direct action, mainly of the IJ23 analog. Some compounds were able to modify the action of the antibiotics gentamicin and norfloxacin against multi-drug resistant strains, indicating a possible synergistic action.

CONCLUSIONS

Among all the results obtained in the study, the relevance of thiadiazine analogs as possible coadjuvant drugs in the antibacterial, anti-inflammatory, and neurological action with low toxicity is clear. Need for further studies to verify these effects in living organisms is not ruled out.

In Silico Study in MPO and Molecular Docking of the Synthetic Drynaran Analogues Against the Chronic Tinnitus: Modulation of the M1 Muscarinic Acetylcholine Receptor

Tinnitus is a syndrome that affects the human auditory system and is characterized by a perception of sounds in the absence of acoustic stimuli, or in total silence. Research indicates that muscarinic acetylcholine receptors (mAChRs), especially the M1 type, have a fundamental role in the alterations of auditory perceptions of tinnitus. Here, a series of computer-aided tools were used, from molecular surface analysis software to services available on the web for estimating pharmacokinetics and pharmacodynamics. The results infer that the low lipophilicity ligands, that is, the 1a-d alkyl furans, present the best pharmacokinetic profile, as compounds with an optimal alignment between permeability and clearance. However, only ligands 1a and 1b have properties that are safe for the central nervous system, the site of cholinergic modulation. These ligands showed similarity with compounds deposited in the European Molecular Biology Laboratory chemical (ChEMBL) database acting on the mAChRs M1 type, the target selected for the molecular docking test. The simulations suggest that the 1 g ligand can form the ligand-receptor complex with the best affinity energy order and that, together with the 1b ligand, they are competitive agonists in relation to the antagonist Tiotropium, in addition to acting in synergism with the drug Bromazepam in the treatment of chronic tinnitus.

Synthesis and anxiolytic effect of europium metallic complex containing lapachol [Eu(DBM)3. LAP] in adult zebrafish through serotonergic neurotransmission: in vivo and in silico approach

Anxiety-related mental health problems are estimated at 3.6% globally, benzodiazepines (BZDs) are the class of drugs indicated for the treatment of anxiety, including lorazepam and diazepam. However, concerns have been raised about the short- and long-term risks associated with BZDs. Therefore, despite anxiolytic and antidepressant drugs, there is a need to develop more effective pharmacotherapies with fewer side effects than existing drugs. The present work reported the synthesis, anxiolytic activity, mechanism of action in Adult Zebrafish (Danio rerio) and in silico study of a europium metallic complex with Lapachol, [Eu(DBM)3. LAP]. Each animal (n = 6/group) was treated intraperitoneally (i.p.; 20 µL) with the synthesized complex (4, 20 and 40 mg/Kg) and with the vehicle (DMSO 3%; 20 µL), being submitted to the tests of locomotor activity and 96h acute toxicity. The light/dark test was also performed, and the serotonergic mechanism (5-HT) was evaluated through the antagonists of the 5-HTR1, 5-HTR2A/2C and 5-HTR3A/3B receptors. The complex was characterized using spectrometric techniques, and the anxiolytic effect of complex may be involved the neuromodulation of receptors 5-HT3A/3B, since the pre-treatment with pizotifen and cyproheptadine did not block the anxiolytic effect of [Eu(DBM)3. LAP], unlike fluoxetine had its anxiolytic effect reversed. In addition, molecular docking showed interaction between the [Eu(DBM)3. LAP] and 5HT3A receptor with binding energy -7.8 kcal/mol and the ADMET study showed that complex has low toxic risk. It is expected that the beginning of this study will allow the application of the new anxiolytic drugs, given the pharmacological potential of the lapachol complex.Communicated by Ramaswamy H. Sarma.

Evaluation of the antibacterial and inhibitory activity of the MepA efflux pump of Staphylococcus aureus by riparins I, II, III, and IV

The efflux pump mechanism contributes to the antibiotic resistance of widely distributed strains of Staphylococcus aureus. Therefore, in the present work, the ability of the riparins N-(4-methoxyphenethyl)benzamide (I), 2-hydroxy-N-[2-(4-methoxyphenyl)ethyl]benzamide (II), 2, 6-dihydroxy-N-[ 2-(4-methoxyphenyl)ethyl]benzamide (III), and 3,4,5-trimethoxy-N-[2-(4-methoxyphenethyl)benzamide (IV) as potential inhibitors of the MepA efflux pump in S. aureus K2068 (fluoroquinolone-resistant). In addition, we performed checkerboard assays to obtain more information about the activity of riparins as potential inhibitors of MepA efflux and also analyzed the ability of riparins to act on the permeability of the bacterial membrane of S. aureus by the fluorescence method with SYTOX Green. A molecular coupling assay was performed to characterize the interaction between riparins and MepA, and ADMET (absorption, distribution, metabolism, and excretion) properties were analyzed. We observed that I-IV riparins did not show direct antibacterial activity against S. aureus. However, combination assays with substrates of MepA, ciprofloxacin, and ethidium bromide (EtBr) revealed a potentiation of the efficacy of these substrates by reducing the minimum inhibitory concentration (MIC). Furthermore, increased EtBr fluorescence emission was observed for all riparins. The checkerboard assay showed synergism between riparins I, II, and III, ciprofloxacin, and EtBr. Furthermore, riparins III and IV exhibited permeability in the S. aureus membrane at a concentration of 200 μg/mL. Molecular docking showed that riparins I, II, and III bound in a different region from the binding site of chlorpromazine (standard pump inhibitor), indicating a possible synergistic effect with the reference inhibitor. In contrast, riparin IV binds in the same region as the chlorpromazine binding site. From the in silico ADMET prediction based on MPO, it could be concluded that the molecules of riparin I-IV present their physicochemical properties within the ideal pharmacological spectrum allowing their preparation as an oral drug. Furthermore, the prediction of cytotoxicity in liver cell lines showed a low cytotoxic effect for riparins I-IV.

Antichagasic evaluation, molecular docking and ADMET properties of the chalcone (2E)-3-(2-fluorophenyl)-1-(2-hydroxy- 3,4,6-trimethoxyphenyl)prop-2-en-1-one against Trypanosoma cruzi

Characterized as a neglected disease, Chagas disease is an infection that, in the current scenario, affects about 8 million people per year, with a higher incidence in underdeveloped countries, Chagas is responsible for physiological disabilities that result in impacts that are slightly reflected in world socioeconomic stability. Although treatments are based on drugs such as Benznidazole, the pathology lacks a continuous treatment method with low toxicological incidence. The present study estimates the anti-chagasic activity of the synthetic chalcone CPN2F based on the alignment between in vitro tests and structural classification in silico studies, molecular docking and ADMET studies. The in vitro tests showed a reduction in the protozoan metabolism in host cells (LLC-MK2). At the same time, the molecular docking models evaluate this growth inhibition through the synergistic effect associated with Benznida- zole against validated therapeutic target key stages (Cruzaine TcGAPDH and Trypanothione reductase) of the Trypanosoma cruzi development cycle. The in silico prediction results reveal an alignment between pharmacokinetic attributes, such as renal absorption and release, which allow the preparation of CPN2F as an antichagasic drug with a low incidence of organic toxicity.Communicated by Ramaswamy H. Sarma.

De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico

This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study of its derivatives as a therapeutic alternative to combat the SARS-CoV-2, pathogen responsible for COVID-19 pandemic, using its S-glycoprotein as a macromolecular target. After the initial screening for the ranking of the products, it was chosen which structure presented the best energy bond with the target. As a result, derivative 4 was submitted to a molecular growth study using artificial intelligence, where 8436 initial structures were obtained that passed through the interaction filters and similarity to the active glycoprotein pocket through the MolAICal computational package. Thus, 557 Hits with active configuration were generated, which is very promising compared to the BLA reference link for inhibiting the biological target. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. The Hit 48 and 250 are the most active compounds against SARS-CoV-2. In summary, the results show that the Hit 250 would be more active than the natural compound, which could be further developed for further testing against SARS-CoV-2. The study employs the de novo approach to design new drugs, combining artificial intelligence and molecular dynamics simulations to create efficient molecular structures. This research aims to contribute to the development of effective therapeutic strategies against the pandemic.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Valtrate, an iridoid compound in Valeriana, elicits anti-glioblastoma activity through inhibition of the PDGFRA/MEK/ERK signaling pathway

BACKGROUND

Valtrate, a natural compound isolated from the root of Valeriana, exhibits antitumor activity in many cancers through different mechanisms. However, its efficacy for the treatment of glioblastoma (GBM), a tumor type with a poor prognosis, has not yet been rigorously investigated.

METHODS

GBM cell lines were treated with valtrate and CCK-8, colony formation and EdU assays, flow cytometry, and transwell, 3D tumor spheroid invasion and GBM-brain organoid co-culture invasion assays were performed to assess properties of proliferation, viability, apoptosis and invasion/migration. RNA sequencing analysis on valtrate-treated cells was performed to identify putative target genes underlying the antitumor activity of the drug in GBM cells. Western blot analysis, immunofluorescence and immunohistochemistry were performed to evaluate protein levels in valtrate-treated cell lines and in samples obtained from orthotopic xenografts. A specific activator of extracellular signal-regulated kinase (ERK) was used to identify the pathways mediating the effect.

RESULTS

Valtrate significantly inhibited the proliferation of GBM cells in vitro by inducing mitochondrial apoptosis and suppressed invasion and migration of GBM cells by inhibiting levels of proteins associated with epithelial mesenchymal transition (EMT). RNA sequencing analysis of valtrate-treated GBM cells revealed platelet-derived growth factor receptor A (PDGFRA) as a potential target downregulated by the drug. Analysis of PDGFRA protein and downstream mediators demonstrated that valtrate inhibited PDGFRA/MEK/ERK signaling. Finally, treatment of tumor-bearing nude mice with valtrate led to decreased tumor volume (fivefold difference at day 28) and enhanced survival (day 27 vs day 36, control vs valtrate-treated) relative to controls.

CONCLUSIONS

Taken together, our study demonstrated that the natural product valtrate elicits antitumor activity in GBM cells through targeting PDGFRA and thus provides a candidate therapeutic compound for the treatment of GBM.

Isolation, characterization and in silico study of propenamide alkaloids from Hymenoepmecis bicolor poison against active μ-opioid receptor

Some insects produce venoms to defend against predators and directly interact with opioid receptors. In the present study, it was identified two alkaloids in the wasp venom species Hymenoepimecis bicolor. It was demonstrated that these could act as potential inhibitors of opioid receptors through their robust affinity to the receptors. The interaction profile was given to opioid receptors (μOR), with 60% of targets similar to alkaloid 1, with 0.25 probability, and 46.7% of targets similar to alkaloid 2, with a probability 0.17 of affinity as a target, which is considered signaling macromolecules and can mediate the most potent analgesic and addictive properties of opiate alkaloids. Notably, both alkaloids showed -7.6 kcal/mol affinity to the morphine agonies through six residues, Gly124, Asp147, Trp293, Ile296, Ile322, and Tyr326. These observations suggest further research on opioid receptors using in vitro studies of possible therapeutic applications.Communicated by Ramaswamy H. Sarma.

Structure-based pharmacophore modeling, virtual screening and simulation studies for the identification of potent anticancerous phytochemical lead targeting cyclin-dependent kinase 2

Cyclin-dependent kinases are of critical importance in directing various cell cycle phases making them as potential tumor targets. Cyclin-dependent kinase 2 (CDK2) in particular plays a significant part during cell cycle events and its imbalance roots out tumorogenic environment. Herein, we built a structure-based pharmacophore model complementing the ATP pocket site of CDK2 with four pharmacophoric features, using a series of structures obtained from cluster analysis during MD simulation assessment. This was followed by its validation and further database screening against Taiwan indigenous plants database (5284 compounds). The screened compounds were subjected toward Lipinski's rule (RO5) and ADMET filter followed by docking analysis and simulation study. In filtering hits (10 compounds) via molecular docking against CDK2, Schinilenol with -8.1 kcal/mol fetched out as a best lead phytoinhibitor in the presence of standard drug (Dinaciclib). Additionally, pharmacophore mapping analysis also indicated relative fit values of dinaciclib and schinilenol as 2.37 and 2.31, respectively. Optimization, flexibility prediction and the stability of CDK2 in complex with the ligands were also ascertained by means of molecular dynamics for 50 ns, which further proposed schinilenol having better binding stability than dinaciclib with RMSD values ranging from 0.31 to 0.34 nm. Reactivity site, biological activity detection and cardiotoxicity assessment also proposed schinilenol as a better phytolead inhibitor than the existing dinaciclib. Abbreviations: CDK2: Cyclin dependent kinase2; ATP: Adenosine triphosphate; MD: Molecular dynamics, RO5: Rule of five; ADMET: Absorption, distribution, metabolism, and excretion; RMSD: Root mean square deviation; DS: Discovery Studio; SOM: Site of metabolism; RBPM: receptor based pharmacophore model; TIP: Schinilenol; hERG: human Ether-à-go-go - Related GeneCommunicated by Ramaswamy H. Sarma.

Pharmacokinetic Study and Metabolite Identification of 1-(3'-bromophenyl)-heliamine in Rats

Tetrahydroisoquinolines have been widely investigated for the treatment of arrhythmias. 1-(3'-bromophenyl)-heliamine (BH), an anti-arrhythmias agent, is a synthetic tetrahydroisoquinoline. This study focuses on the pharmacokinetic characterization of BH, as well as the identification of its metabolites, both in vitro and in vivo. A UHPLC-MS/MS method was developed and validated to quantify BH in rat plasma with a linear range of 1-1000 ng/mL. The validated method was applied to a pharmacokinetic study in rats. The maximum concentration C_max (568.65 ± 122.14 ng/mL) reached 1.00 ± 0.45 h after oral administration. The main metabolic pathways appeared to be phase-I of demethylation, dehydrogenation, and epoxidation, and phase II of glucuronide and sulfate metabolites. Finally, a total of 18 metabolites were characterized, including 10 phase I metabolites and 8 phase II metabolites. Through the above studies, we have gained a better understanding of the absorption and metabolism of BH in vitro and in vivo, which will provide us with guidance for future in-depth studies on this compound.

Metabolites of Cannabigerol Generated by Human Cytochrome P450s Are Bioactive

The phytocannabinoid cannabigerol (CBG) is the central biosynthetic precursor to many cannabinoids, including Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD). Though the use of CBG has recently witnessed a widespread surge because of its beneficial health effects and lack of psychoactivity, its metabolism by human cytochrome P450s is largely unknown. Herein, we describe comprehensive in vitro and in vivo cytochrome P450 (CYP)-mediated metabolic studies of CBG, ranging from liquid chromatography tandem mass spectrometry-based primary metabolic site determination, synthetic validation, and kinetic behavior using targeted mass spectrometry. These investigations revealed that cyclo-CBG, a recently isolated phytocannabinoid, is the major metabolite that is rapidly formed by selected human cytochrome P450s (CYP2J2, CYP3A4, CYP2D6, CYP2C8, and CYP2C9). Additionally, in vivo studies with mice administered with CBG supported these studies, where cyclo-CBG is the major metabolite as well. Spectroscopic binding studies along with docking and modeling of the CBG molecule near the heme in the active site of P450s confirmed these observations, pointing at the preferred site selectivity of CBG metabolism at the prenyl chain over other positions. Importantly, we found out that CBG and its oxidized CBG metabolites reduced inflammation in BV2 microglial cells stimulated with LPS. Overall, combining enzymological studies, mass spectrometry, and chemical synthesis, we showcase that CBG is rapidly metabolized by human P450s to form oxidized metabolites that are bioactive.

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Novel Epoxides of Soloxolone Methyl: An Effect of the Formation of Oxirane Ring and Stereoisomerism on Cytotoxic Profile, Anti-Metastatic and Anti-Inflammatory Activities In Vitro and In Vivo

It is known that epoxide-bearing compounds display pronounced pharmacological activities, and the epoxidation of natural metabolites can be a promising strategy to improve their bioactivity. Here, we report the design, synthesis and evaluation of biological properties of αO-SM and βO-SM, novel epoxides of soloxolone methyl (SM), a cyanoenone-bearing derivative of 18βH-glycyrrhetinic acid. We demonstrated that the replacement of a double-bound within the cyanoenone pharmacophore group of SM with α- and β-epoxide moieties did not abrogate the high antitumor and anti-inflammatory potentials of the triterpenoid. It was found that novel SM epoxides induced the death of tumor cells at low micromolar concentrations (IC₅₀^(24h) = 0.7–4.1 µM) via the induction of mitochondrial-mediated apoptosis, reinforced intracellular accumulation of doxorubicin in B16 melanoma cells, probably by direct interaction with key drug efflux pumps (P-glycoprotein, MRP1, MXR1), and the suppressed pro-metastatic phenotype of B16 cells, effectively inhibiting their metastasis in a murine model. Moreover, αO-SM and βO-SM hampered macrophage functionality in vitro (motility, NO production) and significantly suppressed carrageenan-induced peritonitis in vivo. Furthermore, the effect of the stereoisomerism of SM epoxides on the mentioned bioactivities and toxic profiles of these compounds in vivo were evaluated. Considering the comparable antitumor and anti-inflammatory effects of SM epoxides with SM and reference drugs (dacarbazine, dexamethasone), αO-SM and βO-SM can be considered novel promising antitumor and anti-inflammatory drug candidates.

Combined study of docking and molecular dynamics against DNV-3 SN1 protein by bixinoids

Dengue (DENV), Zica virus (ZIKV), and Chikungunya fever (CHIK) are tropical diseases that have caused a lot of problems in general worldwide. Transmitted by mosquitoes of the species Aedes aegypti and albopictus, they have not been completely eradicated in the country, and their proliferation has only increased in the Northeast region. Within the structure of the virus, it is possible to verify the presence of glycoprotein SN1, which is responsible for its replication. If this macromolecule is inhibited using a specific or complex linker, it can interrupt its replication activity. An alternative to this problem has been using structures derived from natural products that have pharmacological properties. A dynamic and molecular docking combined study used computational simulation in the four isomeric forms of bixin against the SN1 protein. The Z,E-bixin and E,E-bixin isomers, both with affinity energy -6.7 and -6.5 Kcal/mol, presented the best results. Thus, bixin and its isomers, found in annatto seeds, maybe an initial proposal in the search for prototype compounds to study to fight this lethal virus in the future.Communicated by Ramaswamy H. Sarma.

In Silico Tools and Software to Predict ADMET of New Drug Candidates

Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money followed by rational designing of drugs as well as controlled synthesis of those "Hits" which show drug-likeness and possess suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile. With globalization of diseases, resistance of drugs over the time and modification of viruses and microorganisms, computational tools, and artificial intelligence are the future of drug design and one of the important areas where the principles of sustainability and green chemistry (GC) perfectly fit. Most of the new drug entities fail in the clinical trials over the issue of drug-associated human toxicity. Although ecotoxicity related to new drugs is rarely considered, but this is the high time when ecotoxicity prediction should get equal importance along with human-associated drug toxicity. Thus, the present book chapter discusses the available in silico tools and software for the fast and preliminary prediction of a series of human-associated toxicity and ecotoxicity of new drug entities to screen possibly safer drugs before going into preclinical and clinical trials.

A Comprehensive In Silico Exploration of Pharmacological Properties, Bioactivities, Molecular Docking, and Anticancer Potential of Vieloplain F from Xylopia vielana Targeting B-Raf Kinase

Compounds derived from plants have several anticancer properties. In the current study, one guaiane-type sesquiterpene dimer, vieloplain F, isolated from Xylopia vielana species, was tested against B-Raf kinase protein (PDB: 3OG7), a potent target for melanoma. A comprehensive in silico analysis was conducted in this research to understand the pharmacological properties of a compound encompassing absorption, distribution, metabolism, excretion, and toxicity (ADMET), bioactivity score predictions, and molecular docking. During ADMET estimations, the FDA-approved medicine vemurafenib was hepatotoxic, cytochrome-inhibiting, and non-cardiotoxic compared to the vieloplain F. The bioactivity scores of vieloplain F were active for nuclear receptor ligand and enzyme inhibitor. During molecular docking experiments, the compound vieloplain F has displayed a higher binding potential with −11.8 kcal/mol energy than control vemurafenib −10.2 kcal/mol. It was shown that intermolecular interaction with the B-Raf complex and the enzyme’s active gorge through hydrogen bonding and hydrophobic contacts was very accurate for the compound vieloplain F, which was then examined for MD simulations. In addition, simulations using MM-GBSA showed that vieloplain F had the greatest propensity to bind to active site residues. The vieloplain F has predominantly represented a more robust profile compared to control vemurafenib, and these results opened the road for vieloplain F for its utilization as a plausible anti-melanoma agent and anticancer drug in the next era.

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.

Drug Design: Where We Are and Future Prospects

Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.

Computational Exploration of Anti-Cancer Potential of Guaiane Dimers from Xylopia vielana by Targeting B-Raf Kinase Using Chemo-Informatics, Molecular Docking and MD Simulation Studies

BACKGROUND

Natural products from herbs are prolific to display robust anticancer activities.

OBJECTIVES

In the current study, B-Raf kinase protein (PDB: 3OG7), a potent target for melanoma, was tested against two guaiane-type sesquiterpene dimers, xylopin E-F, obtained from Xylopia vielana.

METHODS

In this work, a systematic in silico study using ADMET analysis, bioactivity score forecasts, molecular docking, and its simulations were conducted to understand compounds' pharmacological properties.

RESULTS

During ADMET predictions of both the compounds, Xylopin E-F has displayed a safer profile in hepatotoxicity, cytochrome inhibition, and only xylopin F displayed as non-cardiotoxic compared to FDA approved drug vemurafenib. Both the compounds were proceeded to molecular docking experiments using Autodock docking software and both the compounds Xylopin E-F have displayed higher binding potential with -11.5Kcal/mol energy compared to control vemurafenib -10.2 Kcal/mol. All the compounds were further evaluated for their MD simulations and their molecular interactions with the B-Raf kinase complex displayed precise interactions with the active gorge of the enzyme by hydrogen bonding.

CONCLUSIONS

Overall, xylopin F had a better profile relative to xylopin E and vemurafenib, and these findings indicated that this bio-molecule could be used as an anti-melanoma agent and as a possible anticancer drug in the future. Therefore, this is a systematic optimized in silico approach to creating an anticancer pathway for guaiane dimers against the backdrop of its potential for future drug development.

Novel drug design and bioinformatics: an introduction

In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.

Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors

The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD’s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads.

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.

Natural dye from Caesalpinia sappan L. heartwood for eco-friendly coloring of recycled paper based packing material and its in silico toxicity analysis

The uses of natural dyes are getting popularized due to the increased awareness regarding the toxicity of many chemical colorants. The chemical colorants are being replaced by the natural colorants for the various industrial applications. The plant-based natural colorants are considered eco-friendly and toxic free. In the present study, we report a natural dye from the heartwood of Caesalpinia sappan suitable for paper based packing materials. This forms the first report on the study of natural dye obtained from the heartwood of C. sappan on paper material. The extracted dye had a good photostability and able to make imprints on recycled paper bags. Moreover, a significant inhibition of bacterial growth was observed at a higher dye concentration of 100 μg mL^-1 against P. aeruginosa which was higher than the standard antibiotics. Growth inhibition was also observed in case of B. subtilis (22 ± 0.17 mm) and K. pneumonia (21 ± 0.53 mm) at 100 μg mL^-1. The dye could be used in making medicated packing materials and have many other bio-potential which was validated through in silico toxicity analysis. The application of such natural dyes in paper material value addition will help in a cleaner and sustainable process during paper recycling.

Reactive Metabolites from Thiazole-Containing Drugs: Quantum Chemical Insights into Biotransformation and Toxicity

Drugs containing thiazole and aminothiazole groups are known to generate reactive metabolites (RMs) catalyzed by cytochrome P450s (CYPs). These RMs can covalently modify essential cellular macromolecules and lead to toxicity and induce idiosyncratic adverse drug reactions. Molecular docking and quantum chemical hybrid DFT study were carried out to explore the molecular mechanisms involved in the biotransformation of thiazole (TZ) and aminothiazole (ATZ) groups leading to RM epoxide, S-oxide, N-oxide, and oxaziridine. The energy barrier required for the epoxidation is 13.63 kcal/mol, that is lower than that of S-oxidation, N-oxidation, and oxaziridine formation (14.56, 17.90, and 20.20, kcal/mol respectively). The presence of the amino group in ATZ further facilitates all the metabolic pathways, for example, the barrier for the epoxidation reaction is reduced by ∼2.5 kcal/mol. Some of the RMs/their isomers are highly electrophilic and tend to form covalent bonds with nucleophilic amino acids, finally leading to the formation of metabolic intermediate complexes (MICs). The energy profiles of these competitive pathways have also been explored.

Overview of Synthetic Cannabinoids ADB-FUBINACA and AMB-FUBINACA: Clinical, Analytical, and Forensic Implications

ADB-FUBINACA and AMB-FUBINACA are two synthetic indazole-derived cannabinoid receptor agonists, up to 140- and 85-fold more potent, respectively, than trans-∆⁹-tetrahydrocannabinol (∆⁹-THC), the main psychoactive compound of cannabis. Synthesised in 2009 as a pharmaceutical drug candidate, the recreational use of ADB-FUBINACA was first reported in 2013 in Japan, with fatal cases being described in 2015. ADB-FUBINACA is one of the most apprehended and consumed synthetic cannabinoid (SC), following AMB-FUBINACA, which emerged in 2014 as a drug of abuse and has since been responsible for several intoxication and death outbreaks. Here, we critically review the physicochemical properties, detection methods, prevalence, biological effects, pharmacodynamics and pharmacokinetics of both drugs. When smoked, these SCs produce almost immediate effects (about 10 to 15 s after use) that last up to 60 min. They are rapidly and extensively metabolised, being the O-demethylated metabolite of AMB-FUBINACA, 2-(1-(4-fluorobenzyl)-1H-indazole-3-carboxamide)-3-methylbutanoic acid, the main excreted in urine, while for ADB-FUBINACA the main biomarkers are the hydroxdimethylpropyl ADB-FUBINACA, hydroxydehydrodimethylpropyl ADB-FUBINACA and hydroxylindazole ADB-FUBINACA. ADB-FUBINACA and AMB-FUBINACA display full agonism of the CB1 receptor, this being responsible for their cardiovascular and neurological effects (e.g., altered perception, agitation, anxiety, paranoia, hallucinations, loss of consciousness and memory, chest pain, hypertension, tachycardia, seizures). This review highlights the urgent requirement for additional studies on the toxicokinetic properties of AMB-FUBINACA and ADB-FUBINACA, as this is imperative to improve the methods for detecting and quantifying these drugs and to determine the best exposure markers in the various biological matrices. Furthermore, it stresses the need for clinicians and pathologists involved in the management of these intoxications to describe their findings in the scientific literature, thus assisting in the risk assessment and treatment of the harmful effects of these drugs in future medical and forensic investigations.

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.

Discovery of drug-like acetylcholinesterase inhibitors by rapid virtual screening of a 6.9 million compound database

Cholinesterase inhibitors remain the mainstay of Alzheimer's disease treatment, and the search for new inhibitors with better efficacy and side effect profiles is ongoing. Virtual screening (VS) is a powerful technique for searching large compound databases for potential hits. This study used a sequential VS workflow combining ligand-based VS, molecular docking and physicochemical filtering to screen for central nervous system (CNS) drug-like acetylcholinesterase inhibitors (AChEIs) amongst the 6.9 million compounds of the CoCoCo database. Eleven in silico hits were initially selected, resulting in the discovery of an AChEI with a K_i of 3.2 µM. In vitro kinetics and in silico molecular dynamics experiments informed the selection of an additional seven analogues. This led to the discovery of two further AChEIs, with K_i values of 2.9 µM and 0.65 µM. All three compounds exhibited reversible, mixed inhibition of acetylcholinesterase. Importantly, the in silico physicochemical filter facilitated the discovery of CNS drug-like compounds, such that all three inhibitors displayed high in vitro blood-brain barrier model permeability.

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C-N couplings, as well as Pauson-Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry

Atom- or bond-level chemical properties of interest in medicinal chemistry, such as drug metabolism and electrophilic reactivity, are important to understand and predict across arbitrary new molecules. Deep learning can be used to map molecular structures to their chemical properties, but the data sets for these tasks are relatively small, which can limit accuracy and generalizability. To overcome this limitation, it would be preferable to model these properties on the basis of the underlying quantum chemical characteristics of small molecules. However, it is difficult to learn higher level chemical properties from lower level quantum calculations. To overcome this challenge, we pretrained deep learning models to compute quantum chemical properties and then reused the intermediate representations constructed by the pretrained network. Transfer learning, in this way, substantially outperformed models based on chemical graphs alone or quantum chemical properties alone. This result was robust, observable in five prediction tasks: identifying sites of epoxidation by metabolic enzymes and identifying sites of covalent reactivity with cyanide, glutathione, DNA and protein. We see that this approach may substantially improve the accuracy of deep learning models for specific chemical structures, such as aromatic systems.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach

Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming. Therefore, prediction models based on diverse approaches have been established in recent decades. Computational prediction of molecular properties has become an important step in drug discovery, aiming to identify potential drug-like candidates and reduce costs. However, limitations related to dataset capacity and algorithm adaptation still place restrictions on the applicability of the related models. In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. Benchmark datasets of aqueous solubility (log S), lipophilicity (log D), and membrane permeability measured using the Caco-2 cell line (log P_app) were constructed by merging and calibrating experimental data from diverse articles and databases. Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. MESN achieves performance improvements over other state-of-the-art methods for the prediction of aqueous solubility, lipophilicity, and membrane permeability. Results were also obtained using several other algorithms and independent validation datasets as a control study. Moreover, a dimension reduction analysis (based on t-distributed stochastic neighbor embedding, t-SNE) and an atomic feature similarity analysis showed that the molecular embeddings extracted from the MESN model exhibit good clustering and diversity. Overall, considering the fundamental role of the data and the superior prediction performance of the model, we highlight the applicability of MESN on benchmark datasets for further utility in drug discovery-related molecular property prediction.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

Open access in silico tools to predict the ADMET profiling of drug candidates

INTRODUCTION

We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

AREAS COVERED

This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

EXPERT OPINION

The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam

Thiazoles are biologically active aromatic heterocyclic rings occurring frequently in natural products and drugs. These molecules undergo typically harmless elimination; however, a hepatotoxic response can occur due to multistep bioactivation of the thiazole to generate a reactive thioamide. A basis for those differences in outcomes remains unknown. A textbook example is the high hepatotoxicity observed for sudoxicam in contrast to the relative safe use and marketability of meloxicam, which differs in structure from sudoxicam by the addition of a single methyl group. Both drugs undergo bioactivation, but meloxicam exhibits an additional detoxification pathway due to hydroxylation of the methyl group. We hypothesized that thiazole bioactivation efficiency is similar between sudoxicam and meloxicam due to the methyl group being a weak electron donator, and thus, the relevance of bioactivation depends on the competing detoxification pathway. For a rapid analysis, we modeled epoxidation of sudoxicam derivatives to investigate the impact of substituents on thiazole bioactivation. As expected, electron donating groups increased the likelihood for epoxidation with a minimal effect for the methyl group, but model predictions did not extrapolate well among all types of substituents. Through analytical methods, we measured steady-state kinetics for metabolic bioactivation of sudoxicam and meloxicam by human liver microsomes. Sudoxicam bioactivation was 6-fold more efficient than that for meloxicam, yet meloxicam showed a 6-fold higher efficiency of detoxification than bioactivation. Overall, sudoxicam bioactivation was 15-fold more likely than meloxicam considering all metabolic clearance pathways. Kinetic differences likely arise from different enzymes catalyzing respective metabolic pathways based on phenotyping studies. Rather than simply providing an alternative detoxification pathway, the meloxicam methyl group suppressed the bioactivation reaction. These findings indicate the impact of thiazole substituents on bioactivation is more complex than previously thought and likely contributes to the unpredictability of their toxic potential.

Cortex dictamni-induced liver injury in mice: The role of P450-mediated metabolic activation of furanoids

Many furan containing compounds have been reported to be toxic resulted from the metabolic activation of the furan ring to reactive metabolite (RM). Cortex Dictamni (CD), a widely used herbal medicine, has been reported to cause severe even fatal hepatotoxicity. The injurious components and mechanism of CD-induced liver injury remain unclear. Our preliminary study showed that dictamnine, one major furanoid in CD, caused mouse liver injury via its reactive epoxide metabolite. Besides dictamnine, the major components of CD are series of bioactivation-alerting furanoids. Thus, we hypothesize that series of furanoids in CD may undergo metabolic activation and play a key role in CD-induced liver injury. Here, a single oral dose of 60 g/kg ethanol extract of CD (ECD) caused severe hepatocellular necrosis in mice at 24 h post-dose. ECD-induced liver injury showed a dose- and time-dependent manner. The hepatotoxic effects could be completely abolished by P450 nonselective inhibitor 1-aminobenzotriazole (ABT) and strongly modulated by other P450 modulators. The furanoids-concentrated fraction of ECD was responsible for the hepatotoxicity. At least ten furanoids with high abundance in ECD, such as obakunone, dictamnine, fraxinellone, limonin, were found to be metabolized to reactive epoxide or cis-enedione. The RM levels were consistent with the liver injury degree. Multiple furanoids, rather than single one, cooperatively contributed to the hepatotoxicity. ECD-induced liver injury could be reproduced by a mixture of pure furanoids. In summary, this study provides toxic component profiles of CD and demonstrates that P450-mediated bioactivation of multiple furanoids is responsible for CD-induced liver injury.