The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization

The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.

A Discovery Strategy for Active Compounds of Chinese Medicine Based on the Prediction Model of Compound-Disease Relationship

An accurate characterization of diseases and compounds is the key to predicting the compound-disease relationship (CDR). However, due to the difficulty of a comprehensive description of CDR, the accuracy of traditional drug development models for large-scale CDR prediction is usually unsatisfactory. In order to solve this problem, we propose a new method that integrates the molecular descriptors of compounds and the symptom descriptors of diseases to build a CDR two-dimensional matrix to predict candidate active compounds. The Matlab software draws grayscale images of CDRs, which are used as a benchmark dataset for training convolutional neural network (CNN) models. The trained model is used to predict candidate antitumor active compounds. Among the AlexNet and GoogLeNet models, we selected the GoogLeNet model for the prediction of active compounds in Chinese medicine, and its Acc, Sen, Pre, F-measure, MCC, and AUC are 0.960, 0.956, 0.965, 0.960, 0.920, and 0.964, respectively. In the prediction results of compounds, 1624 candidate CDRs were found in 124 Chinese medicines. Among them, we obtained 31 features of candidate antitumor active compounds. This method provides new insights for the discovery of candidate active compounds in Chinese medicine.

Dose-effect and structure-activity relationships of haloquinoline toxicity towards Vibrio fischeri

Many quinoline (QL) derivatives are present in the environment and pose potential threats to human health and ecological safety. The acute toxicity of 30 haloquinolines (HQs) was examined using the photobacterium Vibrio fischeri. IC₅₀ values (inhibitory concentration for 50% luminescence elimination) were in the range 5.52 to >200 mg·L^-1. The derivative 5-BrQL exhibited the highest toxicity, with 3-ClQL, 3-BrQL, 4-BrQL, 5-BrQL, 6-BrQL, and 6-IQL all having IC₅₀ values below 10 mg·L^-1. Comparative molecular field analysis modeling based on the steric and electrostatic field properties of the HQs was used to quantify the impact of halogen substituents on their toxicity. QL derivative rings with larger substituents at the 2/8-positions and less negative charge at the 4/5/6/8-positions were positively correlated with acute toxicity towards V. fischeri.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Glioma-Targeted Therapeutics: Computer-Aided Drug Design Prospective

Amongst the several types of brain cancers known to humankind, glioma is one of the most severe and life-threatening types of cancer, comprising 40% of all primary brain tumors. Recent reports have shown the incident rate of gliomas to be 6 per 100,000 individuals per year globally. Despite the various therapeutics used in the treatment of glioma, patient survival rate remains at a median of 15 months after undergoing first-line treatment including surgery, radiation, and chemotherapy with Temozolomide. As such, the discovery of newer and more effective therapeutic agents is imperative for patient survival rate. The advent of computer-aided drug design in the development of drug discovery has emerged as a powerful means to ascertain potential hit compounds with distinctively high therapeutic effectiveness against glioma. This review encompasses the recent advances of bio-computational in-silico modeling that have elicited the discovery of small molecule inhibitors and/or drugs against various therapeutic targets in glioma. The relevant information provided in this report will assist researchers, especially in the drug design domains, to develop more effective therapeutics against this global disease.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Molecular Topology for the Search of New Anti-MRSA Compounds

The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DF_MRSA1, DF_MRSA2, DF_MRSA3 and DF_MRSA4 (DF_MRSA was built in a previous study), all with good statistical parameters, such as Fisher-Snedecor F (>68 in all cases), Wilk's lambda (<0.13 in all cases), and percentage of correct classification (>94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.

Synthesis, antioxidant, antimicrobial and antiviral docking studies of ethyl 2-(2-(arylidene)hydrazinyl)thiazole-4-carboxylates

A series of ethyl 2-(2-(arylidene)hydrazinyl)thiazole-4-carboxylates (2a-r) was synthesized in two steps from thiosemicarbazones (1a-r), which were cyclized with ethyl bromopyruvate to ethyl 2-(2-(arylidene)hydrazinyl)thiazole-4-carboxylates (2a-r). The structures of compounds (2a-r) were established by FT-IR, ¹H- and ¹³C-NMR. The structure of compound 2a was confirmed by HRMS. The compounds (2a-r) were then evaluated for their antimicrobial and antioxidant assays. The antioxidant studies revealed, ethyl 2-(2-(4-hydroxy-3-methoxybenzylidene)hydrazinyl)thiazole-4-carboxylate (2g) and ethyl 2-(2-(1-phenylethylidene)hydrazinyl)thiazole-4-carboxylate (2h) as promising antioxidant agents with %FRSA: 84.46 ± 0.13 and 74.50 ± 0.37, TAC: 269.08 ± 0.92 and 269.11 ± 0.61 and TRP: 272.34 ± 0.87 and 231.11 ± 0.67 μg AAE/mg dry weight of compound. Beside bioactivities, density functional theory (DFT) methods were used to study the electronic structure and properties of synthesized compounds (2a-m). The potential of synthesized compounds for possible antiviral targets is also predicted through molecular docking methods. The compounds 2e and 2h showed good binding affinities and inhibition constants to be considered as therapeutic target for M^pro protein of SARS-CoV-2 (COVID-19). The present in-depth analysis of synthesized compounds will put them under the spot light for practical applications as antioxidants and the modification in structural motif may open the way for COVID-19 drug.

Dapsone is not a Pharmacodynamic Lead Compound for its Aryl Derivatives

BACKGROUND

The relatedness between the linear equations of thermodynamics and QSAR was studied thanks to the recently elucidated crystal structure complexes between sulfonamide pterin conjugates and dihydropteroate synthase (DHPS) together with a published set of thirty- six synthetic dapsone derivatives with their reported entropy-driven activity data. Only a few congeners were slightly better than dapsone.

OBJECTIVE

Our study aimed at demonstrating the applicability of thermodynamic QSAR and to shed light on the mechanistic aspects of sulfone binding to DHPS.

METHODS

To this end ligand docking to DHPS, quantum mechanical properties, 2D- and 3D-QSAR as well as Principle Component Analysis (PCA) were carried out.

RESULTS

The short aryl substituents of the docked pterin-sulfa conjugates were outward oriented into the solvent space without interacting with target residues which explains why binding enthalpy (ΔH) did not correlate with potency. PCA revealed how chemically informative descriptors are evenly loaded on the first three PCs (interpreted as ΔG, ΔH and ΔS), while chemically cryptic ones reflected higher dimensional (complex) loadings.

CONCLUSION

It is safe to utter that synthesis efforts to introduce short side chains for aryl derivatization of the dapsone scaffold have failed in the past. On theoretical grounds we provide computed evidence why dapsone is not a pharmacodynamic lead for drug profiling because enthalpic terms do not change significantly at the moment of ligand binding to target.

Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations

Phosphopantetheine adenylyltransferase (PPAT) has been recognized as a promising target to develop novel antimicrobial agents, which is a hexameric enzyme that catalyzes the penultimate step in coenzyme A biosynthesis. In this work, molecular modeling study was performed with a series of PPAT inhibitors using molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR) and molecular dynamic (MD) simulations to reveal the structural determinants for their bioactivities. Molecular docking study was applied to understand the binding mode of PPAT with its inhibitors. Subsequently, 3D-QSAR model was constructed to find the features required for different substituents on the scaffolds. For the best comparative molecular field analysis (CoMFA) model, the Q² and R² values of which were calculated as 0.702 and 0.989, while they were calculated as 0.767 and 0.983 for the best comparative molecular similarity index analysis model. The statistical data verified the significance and accuracy of our 3D-QSAR models. Furthermore, MD simulations were carried out to evaluate the stability of the receptor-ligand contacts in physiological conditions, and the results were consistent with molecular docking studies and 3D-QSAR contour map analysis. Binding free energy was calculated with molecular mechanics generalized born surface area approach, the result of which coincided well with bioactivities and demonstrated that van der Waals accounted for the largest portion. Overall, our study provided a valuable insight for further research work on the recognition of potent PPAT inhibitors.Communicated by Ramaswamy H. Sarma.

MetStabOn-Online Platform for Metabolic Stability Predictions

Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extreme complexity of metabolic pathways in living organisms. In this study, the platform for in silico qualitative evaluation of metabolic stability, expressed as half-lifetime and clearance was developed. The platform is based on the application of machine learning methods and separate models for human, rat and mouse data were constructed. The compounds' evaluation is qualitative and two types of experiments can be performed-regression, which is when the compound is assigned to one of the metabolic stability classes (low, medium, high) on the basis of numerical value of the predicted half-lifetime, and classification, in which the molecule is directly assessed as low, medium or high stability. The results show that the models have good predictive power, with accuracy values over 0.7 for all cases, for Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk) and Random Forest algorithms. Additionally, for each of the analyzed compounds, 10 of the most similar structures from the training set (in terms of Tanimoto metric similarity) are identified and made available for download as separate files for more detailed manual inspection. The predictive power of the models was confronted with the external dataset, containing metabolic stability assessment via the GUSAR software, leading to good consistency of results for SMOreg and Naïve Bayes (~0.8 on average). The tool is available online.

Effective drug combination for Caenorhabditis elegans nematodes discovered by output-driven feedback system control technique

Infections from parasitic nematodes (or roundworms) contribute to a significant disease burden and productivity losses for humans and livestock. The limited number of anthelmintics (or antinematode drugs) available today to treat these infections are rapidly losing their efficacy as multidrug resistance in parasites becomes a global health challenge. We propose an engineering approach to discover an anthelmintic drug combination that is more potent at killing wild-type Caenorhabditis elegans worms than four individual drugs. In the experiment, freely swimming single worms are enclosed in microfluidic drug environments to assess the centroid velocity and track curvature of worm movements. After analyzing the behavioral data in every iteration, the feedback system control (FSC) scheme is used to predict new drug combinations to test. Through a differential evolutionary search, the winning drug combination is reached that produces minimal centroid velocity and high track curvature, while requiring each drug in less than their EC₅₀ concentrations. The FSC approach is model-less and does not need any information on the drug pharmacology, signaling pathways, or animal biology. Toward combating multidrug resistance, the method presented here is applicable to the discovery of new potent combinations of available anthelmintics on C. elegans, parasitic nematodes, and other small model organisms.