In Silico Approaches in Drug Design

• その他の著者: Santos-Filho, Osvaldo (編集者)
• フォーマット: 電子媒体 図書の章
• 言語: 英語
• 出版事項: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• 主題: Research & information: general; Chemistry; 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; 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; 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; natural-like compounds; library of integrated network-based cellular signatures (LINCS); longevity; gene regulating effects; gene descriptors; molecular fingerprints; 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; MM-GBSA; protein-protein interaction; ERK signalling; cocaine addiction; intellectual disability (ID); autism spectrum disorder (ASD); gated recurrent unit; recurrent neural network; transfer learning; caspase-6; inhibitor; molecular design; computational drug design; deep learning; multiscale; polypharmacology; 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 mechanics Poisson-Boltzmann surface area; Parkinson's disease; catechol-O-methyltransferase; inhibitors; bioinformatics; pharmacophore modeling; cytotoxicity; computational drug discovery; chemical space; parallelization; high-performance computers and accelerators; sulfonamides; arylsulfonamide; anticancer compounds; telomerase inhibitors; structure-based drug design; computer drug design; MolAr; DNA intercalating agents; SARS-CoV-2; main protease, Mpro; docking benchmark; non-steroidal anti-inflammatory drugs; drug discovery; lipoxygenase; cyclooxygenase; Hsp90; cancer; QSAR; 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; ADMET; MD simulation; antimicrobial peptide database; antiviral peptides; database filtering technology; Ebola virus; peptide design; G-quadruplex DNA; TERRA; mass spectrometry; biological assays; mangrove natural products; KRASG12C; ligand-based pharmacophore modeling; computational biology; RVFV; RdRp; structural modeling; GlyT1; schizophrenia; DAT; MD; chagas; leishmaniasis; naphthoquinones; antiprotozoal evaluation; ADME; COVID-19; NSP3; TCM; MD simulations; mutagenesis; artificial intelligence; biased signaling; G protein-coupled receptor; immunology; flavonoids; IDO1; free energy
• オンライン･アクセス: DOAB: download the publication; DOAB: description of the publication

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 cruci...