Novel In Silico Approach to Drug Discovery via Computational Intelligence

被引:17
|
作者
Hecht, David [1 ]
Fogel, Gary B. [2 ]
机构
[1] Southwestern Coll, Chula Vista, CA 91910 USA
[2] Nat Select Inc, San Diego, CA 92121 USA
关键词
DE-NOVO DESIGN; FALCIPARUM DIHYDROFOLATE-REDUCTASE; QUANTITATIVE STRUCTURE-ACTIVITY; COMBINATORIAL CHEMISTRY; LIGAND DESIGN; CANDIDATE STRUCTURES; GENETIC ALGORITHM; HIGH-THROUGHPUT; LEAD DISCOVERY; BINDING-SITES;
D O I
10.1021/ci9000647
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
A computational intelligence drug discovery platform is introduced as an innovative technology designed to accelerate high-throughput drug screening for generalized protein-targeted drug discovery. This technology results in collections of novel small molecule compounds that bind to protein targets as well as details on predicted binding modes and molecular interactions. The approach was tested on dihydrofolate reductase (DHFR) for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development. For this purpose, an initial fragment library was defined, and an automated fragment assembly algorithm was generated. These were combined with a computational intelligence screening tool for prescreening of compounds relative to DHFR inhibition. The entire method was assayed relative to spaces of known DHFR inhibitors and with chemical feasibility in mind, leading to experimental validation in future studies.
引用
收藏
页码:1105 / 1121
页数:17
相关论文
共 50 条
  • [31] Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach
    Shah, Hayat Ali
    Yasmin, Sabina
    Ansari, Mohammad Yousuf
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2025, 117
  • [32] Novel approach to drug discovery - Scaffold-based drug discovery TM
    Ibrahim, PN
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2004, 227 : U41 - U41
  • [33] A Recent Appraisal of Artificial Intelligence and in Silico ADMET Prediction in the Early Stages of Drug Discovery
    Kumar, Avinash
    Kini, Suvarna G.
    Rathi, Ekta
    MINI-REVIEWS IN MEDICINAL CHEMISTRY, 2021, 21 (18) : 2786 - 2798
  • [34] Computational drug discovery
    Ou-Yang, Si-sheng
    Lu, Jun-yan
    Kong, Xiang-qian
    Liang, Zhong-jie
    Luo, Cheng
    Jiang, Hualiang
    ACTA PHARMACOLOGICA SINICA, 2012, 33 (09) : 1131 - 1140
  • [35] Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery
    M H Ung
    C-H Sun
    C-W Weng
    C-C Huang
    C-C Lin
    C-C Liu
    C Cheng
    The Pharmacogenomics Journal, 2017, 17 : 351 - 359
  • [36] Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery
    Ung, M. H.
    Sun, C-H
    Weng, C-W
    Huang, C-C
    Lin, C-C
    Liu, C-C
    Cheng, C.
    PHARMACOGENOMICS JOURNAL, 2017, 17 (04): : 351 - 359
  • [37] Computational drug discovery
    Si-sheng Ou-Yang
    Jun-yan Lu
    Xiang-qian Kong
    Zhong-jie Liang
    Cheng Luo
    Hualiang Jiang
    Acta Pharmacologica Sinica, 2012, 33 : 1131 - 1140
  • [38] Editorial: Computer-aided drug design: Drug discovery, computational modelling, and artificial intelligence
    Ye, Fei
    Lin, Min
    Jin, Jia
    Broussy, Sylvain
    FRONTIERS IN CHEMISTRY, 2022, 10
  • [39] Applications of computational biochemistry techniques in the discovery of drug candidates: in silico, in vitro, and in vivo studies
    Alves Bueno, Paulo Sergio
    Vicente Seixas, Flavio Augusto
    BIOPHYSICAL REVIEWS, 2021, 13 (06) : 1337 - 1337
  • [40] A Computational Approach for the Discovery of Novel DNA Methyltransferase Inhibitors
    Kritsi, Eftichia
    Christodoulou, Paris
    Tsiaka, Thalia
    Georgiadis, Panagiotis
    Zervou, Maria
    CURRENT ISSUES IN MOLECULAR BIOLOGY, 2024, 46 (04) : 3394 - 3407