A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques

Onay A., Onay M.

CURRENT COMPUTER-AIDED DRUG DESIGN, cilt.16, sa.4, ss.407-419, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 4
  • Basım Tarihi: 2020
  • Doi Numarası: 10.2174/1573409915666190716143601
  • Dergi Adı: CURRENT COMPUTER-AIDED DRUG DESIGN
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Biotechnology Research Abstracts, Chemical Abstracts Core, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.407-419
  • Anahtar Kelimeler: Drug design, molecular descriptors, artificial neural network, ADRIANA.Code, data mining, frequent subgraph mining, FEATURE-SELECTION, COMPUTATIONAL METHODS, VECTOR MACHINES, DISCOVERY, CLASSIFICATION, DESIGN, PHARMACOVIGILANCE, SOLUBILITY, PREDICTION, DISEASE
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Background: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones.