Computer-Aided Drug Design

Bharatam, Prasad V.

doi:10.1007/978-981-15-5534-3_6

Cited by 22 publications

(8 citation statements)

References 130 publications

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“…Even though there exist a few fully autonomous computational algorithms and tools, these are tarnished by major pitfalls. For instance, these are capable of accomplishing solo commands at a given time and are usually confined to comparatively simple molecular drug targets ( Song et al, 2009 ; Prachayasittikul et al, 2015 ; Bharatam, 2021 ). A highly acclaimed synthesis route design tool for complex natural products is Chematica ( Mikulak-Klucznik et al, 2020 ).…”

Section: Translational Informatics For Investigation Of Potential Nat...mentioning

confidence: 99%

Translational Informatics for Natural Products as Antidepressant Agents

Singla

Joon

Shen

et al. 2022

Front. Cell Dev. Biol.

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Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

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Section: Translational Informatics For Investigation Of Potential Nat...mentioning

confidence: 99%

Translational Informatics for Natural Products as Antidepressant Agents

Singla

Joon

Shen

et al. 2022

Front. Cell Dev. Biol.

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“…The goal in drug discovery is to identify molecules (ligands) with the ability to bind to a macro-molecule (receptor) and consequently block the expression or development of a targeted disease [1][2][3]. While experimental drug discovery provides important information on potential drugs, atomic-level details are inaccessible to experimental studies [4]. Computer-aided drug design (CADD) tools, e.g., molecular docking, quantum chemical methods, and molecular dynamics (MD) simulations can be applied to obtain information about the interactions taking place on the atomic-and electronic-level that governs the binding affinity between a ligand and a receptor, e.g., electrostatic and van der Waals (vdW) interactions, as well as the conformational changes of both the ligand and the receptor due to their interaction.…”

Section: Introductionmentioning

confidence: 99%

Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

et al. 2022

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Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

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“…The field of computer-aided/based (in silico or computational) drug design and discovery is a fast-growing approach that has shown several successes and advancements during the last decade [40]. The structural informatics explosion allowed the identification of the exact chemical and physical properties of molecules.…”

Section: Introductionmentioning

confidence: 99%

Isolation and In Silico Anti-SARS-CoV-2 Papain-Like Protease Potentialities of Two Rare 2-Phenoxychromone Derivatives from Artemisia spp.

et al. 2022

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Two rare 2-phenoxychromone derivatives, 6-demethoxy-4`-O-capillarsine (1) and tenuflorin C (2), were isolated from the areal parts of Artemisia commutata and A. glauca, respectively, for the first time. Being rare in nature, the inhibition potentialities of 1 and 2 against SARS-CoV-2 was investigated using multistage in silico techniques. At first, molecular similarity and fingerprint studies were conducted for 1 and 2 against co-crystallized ligands of eight different COVID-19 enzymes. The carried-out studies indicated the similarity of 1 and 2 with TTT, the co-crystallized ligand of COVID-19 Papain-Like Protease (PLP), (PDB ID: 3E9S). Therefore, molecular docking studies of 1 and 2 against the PLP were carried out and revealed correct binding inside the active site exhibiting binding energies of −18.86 and −18.37 Kcal/mol, respectively. Further, in silico ADMET in addition to toxicity evaluation of 1 and 2 against seven models indicated the general safety and the likeness of 1 and 2 to be drugs. Lastly, to authenticate the binding and to investigate the thermodynamic characters, molecular dynamics (MD) simulation studies were conducted on 1 and PLP.

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Computer-Aided Drug Design

Cited by 22 publications

References 130 publications

Translational Informatics for Natural Products as Antidepressant Agents

Translational Informatics for Natural Products as Antidepressant Agents

Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

Isolation and In Silico Anti-SARS-CoV-2 Papain-Like Protease Potentialities of Two Rare 2-Phenoxychromone Derivatives from Artemisia spp.

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