Explainability and white box in drug discovery


KIRBOĞA K. K., Abbasi S., KÜÇÜKSİLLE E. U.

Chemical Biology and Drug Design, cilt.102, sa.1, ss.217-233, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 102 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1111/cbdd.14262
  • Dergi Adı: Chemical Biology and Drug Design
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, EMBASE, MEDLINE, Veterinary Science Database
  • Sayfa Sayıları: ss.217-233
  • Anahtar Kelimeler: artificial intelligence, computational drug discovery, drug development, explainable artificial intelligence
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.