The Rise and Fall of a Scaffold: A Trend Analysis of Scaffolds in the Medicinal Chemistry Literature

Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction

The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecological toxicity assessment based on pre-trained models. By leveraging pre-training techniques and graph neural network models, we establish a highperformance predictive model. Furthermore, we incorporate a variational autoencoder to optimize the model, enabling simultaneous discrimination of toxicity to bees and molecular degradability. Additionally, despite the low similarity between the endogenous hormones in bees and the compounds in our dataset, our model confidently predicts that these hormones are non-toxic to bees, which further strengthens the credibility and accuracy of our model. We also discovered the negative correlation between the degradation and bee toxicity of compounds. In summary, this study presents an ecological toxicity assessment model with outstanding performance. The proposed model accurately predicts the toxicity of chemicals to bees and their degradability capabilities, offering valuable technical support to relevant fields.

Occurrence of "Natural Selection" in Successful Small Molecule Drug Discovery

Published compounds from ChEMBL version 32 are used to seek evidence for the occurrence of "natural selection" in drug discovery. Three measures of natural product (NP) character were applied, to compare time- and target-matched compounds reaching the clinic (clinical compounds in phase 1-3 development and approved drugs) with background compounds (reference compounds). Pseudo-NPs (PNPs), containing NP fragments combined in ways inaccessible by nature, are increasing over time, reaching 67% of clinical compounds first disclosed since 2010. PNPs are 54% more likely to be found in post-2008 clinical versus reference compounds. The majority of target classes show increased clinical compound NP character versus their reference compounds. Only 176 NP fragments appear in >1000 clinical compounds published since 2008, yet these make up on average 63% of the clinical compound's core scaffolds. There is untapped potential awaiting exploitation, by applying nature's building blocks─"natural intelligence"─to drug design.

MPoMA protects against lung epithelial cell injury via p65 degradation

Numerous cases of lung injury caused by viral infection were reported during the coronavirus disease-19 pandemic. While there have been significant efforts to develop drugs that block viral infection and spread, the development of drugs to reduce or reverse lung injury has been a lower priority. This study aimed to identify compounds from a library of compounds that prevent viral infection that could reduce and prevent lung epithelial cell damage. We investigated the cytotoxicity of the compounds, their activity in inhibiting viral spike protein binding to cells, and their activity in reducing IL-8 production in lung epithelial cells damaged by amodiaquine (AQ). We identified N-(4-(4-methoxyphenoxy)-3-methylphenyl)-N-methylacetamide (MPoMA) as a non-cytotoxic inhibitor against viral infection and AQ-induced cell damage. MPoMA inhibited the expression of IL-8, IL-6, IL-1β, and fibronectin induced by AQ and protected against AQ-induced morphological changes. However, MPoMA did not affect basal IL-8 expression in lung epithelial cells in the absence of AQ. Further mechanistic analysis confirmed that MPoMA selectively promoted the proteasomal degradation of inflammatory mediator p65, thereby reducing intracellular p65 expression and p65-mediated inflammatory responses. MPoMA exerted potent anti-inflammatory and protective functions in epithelial cells against LPS-induced acute lung injury in vivo. These findings suggest that MPoMA may have beneficial effects in suppressing viral infection and preventing lung epithelial cell damage through the degradation of p65 and inhibition of the production of inflammatory cytokines.

Synthesis and SAR investigation of biphenylaminoquinoline derivatives with benzyloxy substituents as promising anticancer agents

The biphenyl scaffold represents a prominent privileged structure within the realms of organic chemistry and drug development. Biphenyl derivatives have demonstrated notable biological activities, including antimicrobial, anti-inflammatory, anti-HIV, and the treatment of neuropathic pain. Importantly, their anticancer abilities should not be underestimated. In this context, the present study involves the design and synthesis of a series of biphenyl derivatives featuring an additional privileged structure, namely the quinoline core. We have also diversified the substituents attached to the benzyloxy group at either the meta or para position of the biphenyl ring categorized into two distinct groups: [4,3']biphenylaminoquinoline-substituted and [3,3']biphenylaminoquinoline-substituted compounds. We embarked on an assessment of the cytotoxic activities of these derivatives in colorectal cancer cell line SW480 and prostate cancer cell line DU145 for exploring the structure-activity relationship. Furthermore, we determined the IC50 values of selected compounds that exhibited superior inhibitory effects on cell viability against SW480, DU145 cells, as well as MDA-MB-231 and MiaPaCa-2 cells. Notably, [3,3']biphenylaminoquinoline derivative 7j displayed the most potent cytotoxicity against these four cancer cell lines, SW480, DU145, MDA-MB-231, and MiaPaCa-2, with IC50 values of 1.05 μM, 0.98 μM, 0.38 μM, and 0.17 μM, respectively. This highly promising outcome underscores the potential of [3,3']biphenylaminoquinoline 7j for further investigation as a prospective anticancer agent in future research endeavors.

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks

Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at https://github.com/ur-whitelab/mol.dev, and the model is useable at https://mol.dev.

Database of 4 Million Medicinal Chemistry-Relevant Ring Systems

Central ring systems are the most important part of bioactive molecules. They determine molecule shape, keep substituents in their proper positions, and also influence global molecular properties. In the present study, a database of 4 million medicinal chemistry-relevant ring systems has been created, not by crude random enumeration but by applying a set of rules derived by analyzing rings present in bioactive molecules. The aromatic properties and tautomer stability of generated rings have also been considered to ensure that the rings in the database are stable and chemically reasonable. 99.2% of these rings are novel and not included in molecules in the ChEMBL or PubChem databases. This large database of ring systems has been created with the goal to provide support for bioisosteric design and scaffold hopping as well as to be used in generative chemistry applications. The complete set of created rings is available for download in the SMILES format from https://peter-ertl.com/molecular/data/.

The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.

Identification of novel 3-aryl-1-aminoisoquinolines-based KRASG12C inhibitors: Rational drug design and expedient construction by CH functionalization/annulation

Developing a synthetic methodology to expediently construct a specific drug scaffold with the desired biological activity remains challenging. Herein, we describe a work on rational application of a synthetic methodology in the synthesis of KRASG12C inhibitors. Novel KRASG12C inhibitors were initially designed with 1-amino-3-aryl isoquinoline scaffold using structure-based drug design strategy. A ruthenium-catalyzed direct monoCH functionalization/annulation cascade reaction of amidines and sulfoxonium ylides was then developed with high versatility of substrates and good tolerance for polar functional groups. By using this reaction, the target compounds 1-amino-3-aryl isoquinolines were facilely prepared. Further in vitro tests led to identification of two novel lead compounds with KRASG12C inhibitory activity.

Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses

Chagas disease is a neglected tropical disease caused by the protozoa Trypanosoma cruzi. Cruzain, its main cysteine protease, is commonly targeted in drug discovery efforts to find new treatments for this disease. Even though the essentiality of this enzyme for the parasite has been established, many cruzain inhibitors fail as trypanocidal agents. This lack of translation from biochemical to biological assays can involve several factors, including suboptimal physicochemical properties. In this work, we aim to rationalize this phenomenon through chemical space analyses of calculated molecular descriptors. These include statistical tests, visualization of projections, scaffold analysis, and creation of machine learning models coupled with interpretability methods. Our results demonstrate a significant difference between the chemical spaces of cruzain and T. cruzi inhibitors, with compounds with more hydrogen bond donors and rotatable bonds being more likely to be good cruzain inhibitors, but less likely to be active on T. cruzi. In addition, cruzain inhibitors seem to occupy specific regions of the chemical space that cannot be easily correlated with T. cruzi activity, which means that using predictive modeling to determine whether cruzain inhibitors will be trypanocidal is not a straightforward task. We believe that the conclusions from this work might be of interest for future projects that aim to develop novel trypanocidal compounds.

Triethylammonium 2-(3-Hydroxy-2-oxoindolin-3-yl)-5,5-dimethyl-3-oxocyclohex-1-en-1-olate

In recent years, the application of privileged structures has become a powerful approach in the discovery of new biologically active molecules. Ion pairing is a strategy used to enhance the permeation of ionized topical drugs. A convenient and efficient method for the synthesis of triethylammonium 2-(3-hydroxy-2-oxoindolin-3-yl)-5,5-dimethyl-3-oxocyclohex-1-en-1-olate has been developed. The presented protocol includes an aldol reaction and the formation of an ammonium salt. Triethylamine is both a reactant and a catalyst in the process. The structure of the synthesized title compound has been established by 1H, 13C-NMR and IR spectroscopy, mass spectrometry, and elemental analysis.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

Discovery of 2-(1-(3-Chloro-4-cyanophenyl)-1H-pyrazol-3-yl)acetamides as Potent, Selective, and Orally Available Antagonists Targeting the Androgen Receptor

The androgen receptor (AR) antagonists are efficient therapeutics for the treatment of prostate cancer (PCa). All the approved AR antagonists to date are targeted to the ligand-binding pocket (LBP) of AR and have suffered from various drug resistances, whereas AR antagonist targeting non-LBP site of AR is conceived as a promising strategy. Through the scaffold hopping of AR LBP antagonists, the 2-chloro-4-(1H-pyrazol-1-yl)benzonitrile was designed as a new core structure for AR antagonists. A total of 46 compounds were synthesized and biologically evaluated to disclose compounds 2f, 2k, and 4c, exhibiting potent AR antagonistic activities (IC₅₀ up to 69 nM), force against antiandrogen resistance, and untraditional targeting site of probably AR binding function 3. Therein, 4c exhibited effective tumor growth inhibition in LNCaP xenograft study upon oral administration. This work provides a novel chemical scaffold for AR antagonists and offers new perspective for the development of PCa therapy.

Trifluoromethylpyridine: An Important Active Fragment for the Discovery of New Pesticides

Trifluoromethylpyridine (TFMP) is a biologically active fragment formed by connecting trifluoromethyl and pyridine ring. As a result of its unique physical and chemical properties and outstanding biological activity, a variety of pesticide compounds with the TFMP fragment have been discovered and marketed and have played important roles in crop protection research. It is therefore a timely and valuable task to summarize the rationality on how to create new molecules containing TFMP fragments based on the structure-activity relationships, design mentality, and potential mechanism. This review gives a brief summary on the pesticides containing TFMP fragments in the past 5 years and introduces the latest progress of our group in this field. The aim is to provide readers with a convenient route to touch this topic and hopefully serve some educational purpose for graduate students as well.

Using Human Serum Albumin Binding Affinities as a Proactive Strategy to Affect the Pharmacodynamics and Pharmacokinetics of Preclinical Drug Candidates

We report on a new preclinical drug optimization strategy that measures drug candidates' binding affinity with human serum albumin (HSA) as an assessment of increasing or decreasing serum T1/2. Three common scaffolds were used as drug prototypes. Common polar and nonpolar substituents attached to the scaffolds have been identified as opportunities for increasing or decreasing the HSA binding affinity. This approach of adjusting HSA binding could be proactively established for preclinical drug candidates by appending functionality to sites on the drug scaffold not involved in biological target interactions. This strategy complements other medicinal chemistry efforts to identify longer or shorter human dosing regimens.

Organosulphur and organoselenium compounds as emerging building blocks for catalytic systems for O-arylation of phenols, a C-O coupling reaction

Diaryl ethers form an important class of organic compounds. The classic copper-mediated Ullmann diaryl ether synthesis has been known for many years and involves the coupling of phenols with aryl halides. However, the use of high reaction temperature, high catalyst loading and expensive ligands has created a need for the development of alternative catalytic systems. In the recent past, organosulphur and organoselenium compounds have been used as building blocks for developing homogeneous, heterogeneous and nanocatalysts for this C-O coupling reaction. Homogeneous catalytic systems include preformed complexes of metals with organosulphur and organoselenium ligands. The performance of such complexes is influenced dramatically by the nature of the chalcogen (S or Se) donor site of the ligand. Nanocatalytic systems (including Pd₁₇Se₁₅, Pd₁₆S₇ and Cu_1.8S) have been designed using a single-source precursor route. Heterogeneous catalytic systems contain either metal (Cu or Pd) or metal chalcogenides (Pd₁₇Se₁₅ or Cu_1.8S) as catalytically active species. This article aims to cover the simple and straightforward methodologies and approaches that are adopted for developing catalytically relevant organosulfur and organoselenium ligands, their homogeneous metal complexes, heterogeneous and nanocatalysts. The effects of chalcogen (S or Se) donor, halogen (Cl/Br/I) of aryl halide, nature (electron withdrawing or electron donating) of substituents present on the aromatic ring of aryl halides or substituted phenols and position (ortho or para) of substitution on the results of catalytic reactions have been critically analyzed and summarized. The effect of composition (Pd₁₇Se₁₅ or Pd₁₆S₇) on the performance of nanocatalytic systems is also highlighted. Substrate scope has also been discussed in all three types of catalysis. The superiority of heterogeneous catalytic systems (e.g., Pd₁₇Se₁₅ immobilised on graphene oxide) indicates the bright future possibilities for the development of efficient catalytic systems using similar or tailored ligands for this reaction.

Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization

With the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as msld_py_prep, to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that msld_py_prep greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.

Evaluation of Anticancer and Antibacterial Activity of Four 4-Thiazolidinone-Based Derivatives

Heterocycles are commonly known for their unique features, e.g., antibacterial or anticancer properties. Although many synthetic heterocycles, such as 4-thiazolidinone (4-TZD), have been synthesized, their potential applications have not yet been fully investigated. However, many researchers have reported relevant results that can be a basis for the search for new potential drugs. Therefore, the aim of this study was to evaluate the cytotoxic, cytostatic, and antibacterial effects of certain 4-thiazolidinone-based derivatives, Les-3166, Les-5935, Les-6009, and Les-6166, on human fibroblasts (BJ), neuroblastoma (SH-SY5Y), epithelial lung carcinoma (A549), and colorectal adenocarcinoma (CACO-2) cell lines in vitro. All tested compounds applied in a concentration range from 10 to 100 µM were able to decrease metabolic activity in the BJ, A549, and SH-SY5Y cell lines. However, the action of Les-3166 was mainly based on the ROS-independent pathway, similarly to Les-6009. In turn, Les-5935 and Les-6166 were able to promote ROS production in BJ, A549, and SH-SY5Y cells, compared to the control. Les-3166, Les-6009, and Les-6166 significantly increased the caspase-3 activity, especially at the concentrations of 50 µM and 100 µM. However, Les-5935 did not induce apoptosis. Only Les-5935 showed a minor cytostatic effect on SH-SY5Y cells. Additionally, the antibacterial properties of the tested compounds against P. aeruginosa bacterial biofilm can be ranked as follows: Les-3166 > Les-5935 > Les-6009. Les-6166 did not show any anti-biofilm activity. In summary, the study showed that Les-5935, Les-6009, and Les-6166 were characterized by anticancer properties, especially in the human lung cancer cell. In cases of BJ, SH-SY5Y, and CACO-2 cells the anticancer usage of such compounds is limited due to effect visible only at 50 and 100 µM.

Cheminformatics analysis of molecular datasets of transcription factors associated with quorum sensing in Pseudomonas aeruginosa

There are molecular structural features that are key to defining the agonist or antagonist activity on LasR, RhlR and PqsR transcription factors, associated with quorum sensing in Pseudomonas aeruginosa.

Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking

Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (M^pro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.

Discovery of 3,4-dichloro-N-(1H-indol-5-yl)benzamide: A highly potent, selective, and competitive hMAO-B inhibitor with high BBB permeability profile and neuroprotective action

Since there is no disease-modifying treatment discovered yet for Parkinson's disease (PD), there is still a vital need to develop novel selective monoamine oxidase B (MAO-B) inhibitors as promising therapeutically active candidates for PD patients. Herein, we report the design, synthesis, and full characterization of new twenty-six indole derivatives as potential human MAO-B (hMAO-B) selective inhibitors. Six compounds (2i, 3b-e, and 5) exhibited low micromolar to nanomolar inhibitory activities over hMAO-B; compared to our recently reported N-substituted indole-based lead compound VIII (hMAO-B IC₅₀ = 777 nM), compound 5 (3,4-dichloro-N-(1H-indol-5-yl)benzamide) exhibited 18-fold increase in potency (IC₅₀ = 42 nM). A selectivity study over hMAO-A revealed an excellent selectivity index of compound 5 (SI > 2375) with a 47-fold increase compared to rasagiline (II, a well-known MAO-B inhibitor, SI > 50). A further kinetic evaluation of compound 5 over hMAO-B showed a reversible and competitive mode of inhibition with K_i value of 7 nM. Highly effective permeability and high CNS bioavailability of compound 5 with P_e = 54.49 × 10^-6 cm/s were demonstrated. Compound 5 also exhibited a low cytotoxicity profile and a promising neuroprotective effect against the 6-hydroxydopamine-induced neuronal cell damage in PC12 cells, which was more effective than that of rasagiline. Docking simulations on both hMAO-B and hMAO-A supported the in vitro data and served as further molecular evidence. Accordingly, we report the discovery of compound 5 as one of the most potent indole-based MAO-B inhibitors to date which is noteworthy to be further evaluated as a promising agent for PD treatment.

TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery

Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.

Ubiquitin-proteasome system and the role of its inhibitors in cancer therapy

Despite all the other cells that have the potential to prevent cancer development and metastasis through tumour suppressor proteins, cancer cells can upregulate the ubiquitin–proteasome system (UPS) by which they can degrade tumour suppressor proteins and avoid apoptosis. This system plays an extensive role in cell regulation organized in two steps. Each step has an important role in controlling cancer. This demonstrates the importance of understanding UPS inhibitors and improving these inhibitors to foster a new hope in cancer therapy. UPS inhibitors, as less invasive chemotherapy drugs, are increasingly used to alleviate symptoms of various cancers in malignant states. Despite their success in reducing the development of cancer with the lowest side effects, thus far, an appropriate inhibitor that can effectively inactivate this system with the least drug resistance has not yet been fully investigated. A fundamental understanding of the system is necessary to fully elucidate its role in causing/controlling cancer. In this review, we first comprehensively investigate this system, and then each step containing ubiquitination and protein degradation as well as their inhibitors are discussed. Ultimately, its advantages and disadvantages and some perspectives for improving the efficiency of these inhibitors are discussed.

Discovery of N-(1-(3-fluorobenzoyl)-1H-indol-5-yl)pyrazine-2-carboxamide: a novel, selective, and competitive indole-based lead inhibitor for human monoamine oxidase B

Herein, two new series of N-substituted indole-based analogues were rationally designed, synthesized via microwave heating technology, and evaluated as noteworthy MAO-B potential inhibitors. Compared to the reported indazole-based hits VI and VII, compounds 4b and 4e exhibited higher inhibitory activities over MAO-B with IC₅₀ values of 1.65 and 0.78 µM, respectively. When compared to the modest selectivity index of rasagiline (II, a well-known MAO-B inhibitor, SI > 50), both 4b and 4e also showed better selectivity indices (SI > 60 and 120, respectively). A further kinetic evaluation of the most potent derivative (4e) displayed a competitive mode of inhibition (inhibition constant (K_i)/MAO-B = 94.52 nM). Reasonable explanations of the elicited biological activities were presented via SAR study and molecular docking simulation. Accordingly, the remarkable MAO-B inhibitory activity of 4e (N-(1-(3-fluorobenzoyl)-1H-indol-5-yl)pyrazine-2-carboxamide), with its selectivity and competitive inhibition, advocates its potential role as a promising lead worthy of further optimization.

Overview of the SAMPL6 pKa challenge: evaluating small molecule microscopic and macroscopic pKa predictions

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Enantiomeric Resolution and Absolute Configuration of a Chiral δ-Lactam, Useful Intermediate for the Synthesis of Bioactive Compounds

During the past several years, the frequency of discovery of new molecular entities based on γ- or δ-lactam scaffolds has increased continuously. Most of them are characterized by the presence of at least one chiral center. Herein, we present the preparation, isolation and the absolute configuration assignment of enantiomeric 2-(4-bromophenyl)-1-isobutyl-6-oxopiperidin-3-carboxylic acid (trans-1). For the preparation of racemic trans-1, the Castagnoli-Cushman reaction was employed. (Semi)-preparative enantioselective HPLC allowed to obtain enantiomerically pure trans-1 whose absolute configuration was assigned by X-ray diffractometry. Compound (+)-(2R,3R)-1 represents a reference compound for the configurational study of structurally related lactams.

Synthesis, biological evaluation, and molecular docking analysis of phenstatin based indole linked chalcones as anticancer agents and tubulin polymerization inhibitors

A library of new phenstatin based indole linked chalcone compounds (9a-z and 9aa-ad) were designed and synthesized. Of these, compound 9a with 1-methyl, 2- and 3-methoxy substituents in the aromatic ring was efficacious against the human oral cancer cell line SCC-29B, spheroids, and in a mouse xenograft model of oral cancer AW13516. Compound 9a exhibited anti-cancer activity through disrupting cellular integrity and affecting glucose metabolism-which is a hallmark of cancer. The cellular architecture was affected by inhibition of tubulin polymerization as observed by an immunofluorescence assay on 9a-treated SCC-29B cells. An in vitro tubulin polymerization kinetics assay provided evidence of direct interaction of 9a with tubulin. This physical interaction between tubulin and compound 9a was further confirmed by Surface Plasmon Resonance (SPR) analysis. Molecular docking experiments and validations revealed that compound 9a interacts and binds at the colchicine binding site of tubulin and at active sites of key enzymes in the glucose metabolism pathway. Based on in silico modeling, biophysical interactions, and pre-clinical observations, 9a consisting of phenstatin based indole-chalcone scaffolds, can be considered as an attractive tubulin polymerization inhibitor candidate for developing anti-cancer therapeutics.

Moving targets in drug discovery

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disability, and schizophrenia are increasingly investigated in recent years. The methodology enables researchers to capture trends in research attention in target space at an early stage during the drug discovery process. Workflows, scripts, and data used in this study are publicly available from https://github.com/BZdrazil/Moving_Targets. An interactive web application allows the customized exploration of target, biological process, and disease trends (available at https://rguha.shinyapps.io/MovingTargets/).

Pharmacoinformatic Investigation of Medicinal Plants from East Africa

Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing ∼6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

Diaryl Ether: A Privileged Scaffold for Drug and Agrochemical Discovery

Diaryl ether (DE) is a functional scaffold existing widely both in natural products (NPs) and synthetic organic compounds. Statistically, DE is the second most popular and enduring scaffold within the numerous medicinal chemistry and agrochemical reports. Given its unique physicochemical properties and potential biological activities, DE nucleus is recognized as a fundamental element of medicinal and agrochemical agents aimed at different biological targets. Its drug-like derivatives have been extensively synthesized with interesting biological features including anticancer, anti-inflammatory, antiviral, antibacterial, antimalarial, herbicidal, fungicidal, insecticidal, and so on. In this review, we highlight the medicinal and agrochemical versatility of the DE motif according to the published information in the past decade and comprehensively give a summary of the target recognition, structure-activity relationship (SAR), and mechanism of action of its analogues. It is expected that this profile may provide valuable guidance for the discovery of new active ingredients both in drug and pesticide research.

More Than Resveratrol: New Insights into Stilbene-Based Compounds

The concept of a scaffold concerns many aspects at different steps on the drug development path. In medicinal chemistry, the choice of relevant "drug-likeness" scaffold is a starting point for the design of the structure dedicated to specific molecular targets. For many years, the chemical uniqueness of the stilbene structure has inspired scientists from different fields such as chemistry, biology, pharmacy, and medicine. In this review, we present the outstanding potential of the stilbene-based derivatives. Naturally occurring stilbenes, together with powerful synthetic chemistry possibilities, may offer an excellent approach for discovering new structures and identifying their therapeutic targets. With the development of scientific tools, sophisticated equipment, and a better understanding of the disease pathogenesis at the molecular level, the stilbene scaffold has moved innovation in science. This paper mainly focuses on the stilbene-based compounds beyond resveratrol, which are particularly attractive due to their biological activity. Given the "fresh outlook" about different stilbene-based compounds starting from stilbenoids with particular regard to isorhapontigenin and methoxy- and hydroxyl- analogues, the update about the combretastatins, and the very often overlooked and underestimated benzanilide analogues, we present a new story about this remarkable structure.

More Than Resveratrol: New Insights into Stilbene-Based Compounds

The concept of a scaffold concerns many aspects at different steps on the drug development path. In medicinal chemistry, the choice of relevant "drug-likeness" scaffold is a starting point for the design of the structure dedicated to specific molecular targets. For many years, the chemical uniqueness of the stilbene structure has inspired scientists from different fields such as chemistry, biology, pharmacy, and medicine. In this review, we present the outstanding potential of the stilbene-based derivatives. Naturally occurring stilbenes, together with powerful synthetic chemistry possibilities, may offer an excellent approach for discovering new structures and identifying their therapeutic targets. With the development of scientific tools, sophisticated equipment, and a better understanding of the disease pathogenesis at the molecular level, the stilbene scaffold has moved innovation in science. This paper mainly focuses on the stilbene-based compounds beyond resveratrol, which are particularly attractive due to their biological activity. Given the "fresh outlook" about different stilbene-based compounds starting from stilbenoids with particular regard to isorhapontigenin and methoxy- and hydroxyl- analogues, the update about the combretastatins, and the very often overlooked and underestimated benzanilide analogues, we present a new story about this remarkable structure.

The Symbiotic Relationship Between Drug Discovery and Organic Chemistry

All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high-quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity-, biology-, lead-, or fragment-oriented syntheses; and DNA-encoded libraries) are critically surveyed.

Is it time for artificial intelligence to predict the function of natural products based on 2D-structure

Currently, there is no established technique that allows the function of a compound produced by nature to be predicted by looking at its 2-dimensional chemical structure. One of chemistry's grand challenges: to find a function for every known metabolite. We explore the opportunity for Artificial Intelligence to provide rationale interrogation of metabolites to predict their function.

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases¹, heuristics² and social influences³. These anthropogenic chemical reaction data are widely used to train machine-learning models⁴ that are used to predict organic⁵ and inorganic^6,7 syntheses. However, it is known that societal biases are encoded in datasets and are perpetuated in machine-learning models⁸. Here we identify as-yet-unacknowledged anthropogenic biases in both the reagent choices and reaction conditions of chemical reaction datasets using a combination of data mining and experiments. We find that the amine choices in the reported crystal structures of hydrothermal synthesis of amine-templated metal oxides⁹ follow a power-law distribution in which 17% of amine reactants occur in 79% of reported compounds, consistent with distributions in social influence models^10-12. An analysis of unpublished historical laboratory notebook records shows similarly biased distributions of reaction condition choices. By performing 548 randomly generated experiments, we demonstrate that the popularity of reactants or the choices of reaction conditions are uncorrelated to the success of the reaction. We show that randomly generated experiments better illustrate the range of parameter choices that are compatible with crystal formation. Machine-learning models that we train on a smaller randomized reaction dataset outperform models trained on larger human-selected reaction datasets, demonstrating the importance of identifying and addressing anthropogenic biases in scientific data.

Rational design of novel sirtuin 1 activators via structure-activity insights from application of QSAR modeling

Sirtuin 1 (SIRT1) enzyme regulates major cell activities, and its activation offers lucrative therapeutic potentials for aging diseases including Alzheimer's disease (AD). Regarding the global aging society, continual attention has been given to various chemical scaffolds as a source for the discovery of novel SIRT1 activators since the discovery of the pioneer activator, resveratrol. Understanding structure-activity relationship (SAR) is essential for screening, designing as well as improving the properties of drugs. In this study, an in silico approach based on quantitative structure-activity relationship (QSAR) modeling, was employed for understanding the SAR of currently available SIRT1 fused-aromatic activators (i.e., imidazothiazole, oxazolopyridine, and azabenzimidazole analogs). Three QSAR models constructed using multiple linear regression (MLR) provided good predictive performance (R ² _LOOCV = 0.729 - 0.863 and RMSE _LOOCV = 0.165 - 0.325). An additional novel set of 181 structurally modified compounds were rationally designed according to key descriptors deduced from the QSAR findings and their SIRT1 activities were predicted using the constructed models. In overview, the study provides insightful SAR findings of currently available SIRT1 activators that would be useful for guiding the rational design, screening, and development of further potent SIRT1 activators for managing age-related clinical conditions. A series of promising compounds as well as important scaffolds and molecular properties for potent SIRT1 activator were highlighted. This study demonstrated the efficacious role of QSAR-driven structural modification for the rational design of novel leads.