A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method
Authors (first, second and last of 4)
Collection
AI and ML for Small Molecule Drug Discovery in the Big Data Era
Advancements in the informatics and omics based technologies have enhanced our ability to generate data at lower costs. The recent emergence of ‘big data’ in chemistry and biology has fundamentally revolutionized molecular biology and drug development paradigms. The recent availability of open data though various databases and online resources has led to simply too much information (big data) for a human being to assimilate using traditional research methods. The emergence of machine learning (ML) and artificial intelligence (AI) offers guidance to the research scientists to process, analyze and understand the data, and their extensive application appears to be the future for drug discovery. The traditional drug discovery process is very costly and lengthy with limited success probability. The chemical space is very large, a fraction of which we have explored. AI can be used to explore the chemical space and to understand the pattern of the complex big data. AI algorithms can make accurate predictions about complex systems involving the vast and unexplored space of molecules, reactions, and biological interactions. ML techniques have potential to identify new drug candidates in much less time than the conventional research. However, the field is still young, having ample scope for improvements in the accuracy of AI algorithms and the adoption of more standardized and rigorous benchmarks so that the discipline can mature and improve further.
This collection on “AI and ML for Small Molecule Drug Discovery in the Big Data Era” showcases the latest developments in this field. Researchers working in this fascinating area are welcome to submit their fine work via the Editorial Manager system for consideration of inclusion in this topical issue which will be available online and continuously updated.
For any query regarding this topical issue, please contact
Prof. Kunal Roy, kunal.roy.modi@gmail.com
Editors
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Kunal Roy, PhD, Jadavpur University, Kolkata, India
Dr. Kunal Roy is Professor & Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India (https://sites.google.com/site/kunalroyindia). He has been a recipient of Commonwealth Academic Staff Fellowship and Marie Curie International Incoming Fellowship (University of Manchester) and a former visiting scientist of IRCCS Mario Negri Institute, Milano. The field of his research interest is Quantitative Structure-Activity Relationship (QSAR) and Molecular Modeling. Dr. Roy has published more than 300 research articles (ORCID: http://orcid.org/0000-0003-4486-8074) in refereed journals (current SCOPUS h index 43).
Articles (57 in this collection)
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Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation
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MLASM: Machine learning based prediction of anticancer small molecules
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Network pharmacology, molecular docking, and molecular dynamics simulation analysis reveal the molecular mechanism of halociline against gastric cancer
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Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis
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A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors
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Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints
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Classification models for predicting the bioactivity of pan-TRK inhibitors and SAR analysis
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Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides
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FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor
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Data mining and molecular dynamics analysis to detect HIV-1 reverse transcriptase RNase H activity inhibitor
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Machine learning-based classification models for non-covalent Bruton’s tyrosine kinase inhibitors: predictive ability and interpretability
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Elucidating the functional impact of G137V and G144R variants in Maroteaux Lamy’s Syndrome by Molecular Dynamics Simulation
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Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches
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Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery
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Classification of FLT3 inhibitors and SAR analysis by machine learning methods
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A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints
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Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study
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Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches
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LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets
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Computational investigation of drug bank compounds against 3C-like protease (3CLpro) of SARS-CoV-2 using deep learning and molecular dynamics simulation
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Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors
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ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors
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Prediction of antischistosomal small molecules using machine learning in the era of big data
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Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network
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Improved machine learning scoring functions for identification of Electrophorus electricus’s acetylcholinesterase inhibitors
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Identification of kinase inhibitors that rule out the CYP27B1-mediated activation of vitamin D: an integrated machine learning and structure-based drug designing approach
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Ensemble learning application to discover new trypanothione synthetase inhibitors
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Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review
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Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level
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Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases
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In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods
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Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking
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A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ
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Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
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Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model
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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database
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Machine learning models for classification tasks related to drug safety
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review
Authors (first, second and last of 4)
