In Silico Approaches in Drug Design / / Osvaldo Santos-Filho.

• VerfasserIn: Santos-Filho, Osvaldo,
• Place / Publishing House: Basel : : MDPI - Multidisciplinary Digital Publishing Institute,, 2022.
• Year of Publication: 2022
• Language: English
• Physical Description: 1 online resource (754 pages)

This reprint is a collection of 31 original papers and four reviews, published from 2021 to 2022, focused on the application of a wide range of computational tools in medicinal chemistry projects: from molecular docking to artificial intelligence approaches. Applications of in silico tools are crucial in the early stages of drug design, such as planning more efficient and economic synthetic routes for chemical administration, screening of huge databases, as well as proposing hypotheses for probable mechanisms of action of drugs in macromolecular targets. Such endeavors are extremely complex and require the usage of modern and sophisticated approaches, such as artificial intelligence, data mining, computational molecular simulations through classical mechanics and quantum mechanics, molecular docking, chemoinformatics, applied mathematics, and biostatistics.

Table of Contents

About the Editor -- Preface to "In Silico Approaches in Drug Design" -- Identification of Potential Allosteric Site Binders of Indoleamine 2,3-Dioxygenase 1 from Plants: A Virtual and Molecular Dynamics Investigation -- In Silico Approaches: A Way to Unveil Novel Therapeutic Drugs for Cervical Cancer Management -- Opportunities and Challenges for In Silico Drug Discovery at Delta Opioid Receptors -- Computational Investigations of Traditional Chinese Medicinal Compounds against the Omicron Variant of SARS-CoV-2 to Rescue the Host Immune System -- In Silico Antiprotozoal Evaluation of 1,4-Naphthoquinone Derivatives against Chagas and Leishmaniasis Diseases Using QSAR, Molecular Docking, and ADME Approaches -- QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia -- Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors -- Drug Design by Pharmacophore and Virtual Screening Approach -- Virtual Screening Based on Machine Learning Explores Mangrove Natural Products as KRASG12C Inhibitors -- Chromene Derivatives as Selective TERRA G-Quadruplex RNA Binders with Antiproliferative Properties -- Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses -- Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches -- Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins -- Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis -- Drug Discovery of New Anti-Inflammatory Compounds by Targeting Cyclooxygenases -- Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro -- Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems -- In Silico Design, Synthesis and Biological Evaluation of Anticancer Arylsulfonamide Endowed with Anti-Telomerase Activity -- A Review on Parallel Virtual Screening Softwares for High-Performance Computers -- Discovery of Small Molecules as Membrane-Bound Catechol-O-methyltransferase Inhibitors with Interest in Parkinson's Disease: Pharmacophore Modeling, Molecular Docking and In -- Vitro Experimental Validation Studies -- Unravelling the Interaction of Piperlongumine with the Nucleotide-Binding Domain of HSP70: A Spectroscopic and In Silico Study -- Plaquevent and Laurent Maveyraud Fragment-Based Ligand Discovery Applied to the Mycolic Acid Methyltransferase Hma (MmaA4) from Mycobacterium tuberculosis: A Crystallographic and Molecular Modelling Study -- A Deep-Learning Proteomic-Scale Approach for Drug Design -- De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach -- A Rational Design of -Helix-Shaped Peptides Employing the Hydrogen-Bond Surrogate Approach: A Modulation Strategy for Ras-RasGRF1 Interaction in Neuropsychiatric Disorders -- Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design -- [-15]In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus -- Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes -- High-Throughput Screening and Molecular Dynamics Simulation of Natural Product-like -- Compounds against Alzheimer's Disease through Multitarget Approach -- In Silico Prediction of Novel Inhibitors of SARS-CoV-2 Main Protease through Structure-Based Virtual Screening and Molecular Dynamic Simulation -- Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors -- Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints -- Rational Design of Novel Inhibitors of -Glucosidase: An Application of Quantitative Structure Activity Relationship and Structure-Based Virtual Screening -- Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics -- A New Computer Model for Evaluating the Selective Binding Affinity of Phenylalkylamines to T-Type Ca2+ Channels.