Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

“…The latest advances in machine learning tools, coupled with the availability of everlarger data sets, brought about a fresh wave for faster and less complicated computationalguided drug discovery efforts [84][85][86]. In this work, we could assemble a large dataset containing 5523 inhibitors of the three AKT isoforms, assayed under a variety of experimental conditions.…”

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

“…In modern days, many different in silico approaches can be used to assist the design of a drug candidate or drug, from data (e.g., text and image) mining (e.g., annotated drug databases, antiviral peptide databases, electronic health patient records…), genome analysis, comparative genomics, multiple sequence alignments, visualization tools for epidemiological studies, analysis of macromolecular interaction networks, structural predictions (e.g., comparative modeling, protein folding…), antibody-drug conjugate, analysis of point mutations, protein docking, various types of molecular simulation engines (e.g., for proteins, peptides, small molecules, cell membrane, DNA, RNA, glycans, and interactions among these molecules…), binding pocket predictions, PROTACs (e.g., degradation of viral protein capsids), transcriptomic profile analysis, virtual screening (from small collections of approved drugs as in drug repositioning or repurposing projects to the screening of ultra-large virtual libraries), hit to lead optimization, drug combination, computational polypharmacology and compound profiling, ADMET prediction, multiparameter optimization methods associated with novel data visualization approaches, systems biology, systems pharmacology, with or without the use of machine learning and artificial intelligence (AI) algorithms depending on the type of methods, available data and the stage of the projects [19] , [77] , [80] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] , [149] , [150] , [151] , [152] , [153] , [154] , [155] , [156] , [157] , [158] , [159] , [160] , [161] , [162] , [163] , [164] , [165] , [166] , [167] , [168] , [169] , …”

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

“…Thus, AI applications related to big data analytics in the pharmaceutical space are witnessing a constant interest in making the multipronged approach of the multifaceted drug development process more promising and less time-consuming. However, some hurdles still need to be overcome despite numerous advancements, leaving sufficient room for further data-driven AI-led innovations [ 11 ].…”

“…The artificial intelligence approach has enabled the development of drug candidates in a more structured and economical manner and within a considerably shorter time period. The computational resources and algorithms in the drug discovery process utilize existing data to provide better analytics and assessment, from identifying a drug candidate to the pharmaceutical industry’s manufacturing process [ 11 – 13 ]. Hence, prior to the synthesis and experimental evaluation of the drug molecule, the AI-driven analysis facilitates identifying and screening the drug candidates against the desired disease effectively and efficiently.…”