Reprint
In Silico Approaches in Drug Design
Edited by
October 2022
754 pages
- ISBN978-3-0365-5384-9 (Hardback)
- ISBN978-3-0365-5383-2 (PDF)
This is a Reprint of the Special Issue In Silico Approaches in Drug Design that was published in
Biology & Life Sciences
Chemistry & Materials Science
Medicine & Pharmacology
Summary
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.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
T-type calcium channel blocker; homology modeling; computer-aid drug design; virtual drug screening; L-type calcium channel; mTOR kinase; marine natural products; ATP-competitive inhibitors; structure-based pharmacophore modeling; virtual screening; molecular docking; molecular dynamics simulations; binding free energy; in silico ADMET; α-Glucosidase; QSAR modeling; homology modeling; molecular docking; ADMET profiling; cervical cancer management; computer-aided drug design; E6 inhibitors; in silico studies; human papillomavirus; manifold learning; machine learning; rdkit; embeddings; Tox21; principal component analysis; autoencoder; skin sensitization; toxicity prediction; in silico prediction; machine learning; random forest; conformal prediction; bioactivity descriptors; SARS coronavirus; SARS-CoV-2 main protease; structure-based virtual screening; molecular dynamic simulation; hit identification; Alzheimer’s disease; multitarget; molecular dynamics simulations; natural-like compounds; virtual screening; library of integrated network-based cellular signatures (LINCS); longevity; gene regulating effects; gene descriptors; molecular fingerprints; machine learning; deep neural network; drug repurposing; Variola virus; thymidylate kinase; smallpox; docking; molecular dynamics; molecular modeling; permeability; membrane disruption; membrane proteins; drugs; antimicrobial peptides; Ras; RasGRF1; hydrogen-bond surrogate; computational residue scanning; molecular dynamics; MM-GBSA; protein–protein interaction; ERK signalling; cocaine addiction; intellectual disability (ID); autism spectrum disorder (ASD); gated recurrent unit; recurrent neural network; machine learning; transfer learning; caspase-6; inhibitor; molecular design; computational drug design; deep learning; multiscale; polypharmacology; autoencoder; docking; recurrent neural network; Mycobacterium tuberculosis; mycolic acid methyltransferases; fragment-based ligand discovery; binding energies; molecular modelling; heat shock protein; HSP70; nucleotide-binding domain; piperlongumine; fluorescence spectroscopy; circular dichroism; molecular docking; molecular dynamics; molecular mechanics Poisson–Boltzmann surface area; Parkinson’s disease; catechol-O-methyltransferase; inhibitors; bioinformatics; pharmacophore modeling; molecular docking; cytotoxicity; computational drug discovery; virtual screening; molecular docking; chemical space; parallelization; high-performance computers and accelerators; sulfonamides; arylsulfonamide; anticancer compounds; telomerase inhibitors; structure-based drug design; pharmacophore modeling; docking; molecular dynamics; computer drug design; molecular docking; molecular dynamic simulation; virtual screening; MolAr; DNA intercalating agents; molecular docking; molecular dynamics; SARS-CoV-2; main protease, Mpro; docking benchmark; docking; non-steroidal anti-inflammatory drugs; drug discovery; lipoxygenase; cyclooxygenase; Hsp90; cancer; QSAR; machine learning; pharmacophores; in-silico drug design; AlphaFold; anti-CRISPR proteins; prokaryotic defence mechanisms; bacteriophages; structural biology; protein drug; Merkel cell polyomavirus; Merkel cell carcinomas; drug design; molecular docking; ADMET; MD simulation; antimicrobial peptide database; antiviral peptides; database filtering technology; SARS-CoV-2; Ebola virus; peptide design; G-quadruplex DNA; TERRA; docking; circular dichroism; mass spectrometry; biological assays; mangrove natural products; KRASG12C; machine learning; molecular docking; drug discovery; virtual screening; molecular dynamics; structure-based pharmacophore modeling; ligand-based pharmacophore modeling; virtual screening; drug discovery; bioinformatics; computational biology; RVFV; RdRp; structural modeling; virtual screening; docking; MD simulation; GlyT1; QSAR; schizophrenia; ADMET; molecular docking; DAT; MD; chagas; leishmaniasis; naphthoquinones; antiprotozoal evaluation; QSAR; molecular docking; ADME; SARS-CoV-2; COVID-19; NSP3; TCM; MD simulations; mutagenesis; artificial intelligence; computer-aided drug design; molecular dynamic simulation; biased signaling; G protein-coupled receptor; cancer; immunology; flavonoids; IDO1; virtual screening; molecular docking; molecular dynamics; free energy