A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

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The effectiveness of treating bacterial infections is seriously threatened by the emergence of bacterial resistance to chemical treatment. Growth of microbes in biofilm is one of the main causes of resistance to antimicrobial drugs. Quorum sensing (QS) inhibition, which targets the QS signalling system by obstructing cellcell communication, was developed as an alternative treatment by creating innovative anti-biofilm drugs. Therefore, the goal of this study is to develop novel antimicrobial drugs that are effective against Pseudomonas aeruginosa by inhibiting QS and acting as anti-biofilm agents. In this study, N-(2-and 3-pyridinyl)benzamide derivatives were selected to design and syntheses. Antibiofilm activity was revealed by all the synthesized compounds and the biofilm was visibly impaired, and the OD 595nm readings of solubilized biofilm cells presented a momentous difference between the treated and untreated biofilms. The best anti-QS zone was observed for compound 5d and found to be 4.96 mm. Through in silico research, the physicochemical characteristics and binding manner of these produced compounds were examined. For the purpose of understanding the stability of the protein and ligand complex, molecular dynamic simulation was also carried out. The overall findings showed that N-(2-and 3-pyridinyl)benzamide derivatives could be the key to creating effective newer antiquorum sensing drugs that are effective against different bacteria.

Various posttranslational modifications like hyperphosphorylation, O-GlcNAcylation, and acetylation have been attributed to induce the abnormal folding in tau protein.Recent in vitro studies revealed the possible involvement of N-glycosylation of tau protein in the abnormal folding and tau aggregation. Hence, in this study, we performed a microsecond long all atom molecular dynamics simulation to gain insights into the effects of N-glycosylation on Asn-359 residue which forms part of the microtubule binding region. Trajectory analysis of the stimulations coupled with essential dynamics and free energy landscape analysis suggested that tau, in its Nglycosylated form tends to exist in a largely folded conformation having high beta sheet propensity as compared to unmodified tau which exists in a large extended form with very less beta sheet propensity. Residue interaction network analysis of the lowest energy conformations further revealed that Phe378 and Lys353 are the functionally important residues in the peptide which helped in initiating the folding process and Phe378, Lys347, and Lys370 helped to maintain the stability of the protein in the folded state.