Machine Learning in Drug Discovery: A Review
By Suresh Dara, Swetha Dhamercherla, S. S. Jadav, Christy M Babu, M. Ahsan. Artificial Intelligence Review, 2021
625 citations recorded by the source corpus.
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3a191f45f6c0e8ecc598dc349022b253a652b7f2
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Citations in the corpus
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, methodology): “In recent years, AI has become an indispensable tool in accelerating drug discovery, optimizing clinical trials, and personalizing treatments, marking a shift toward more efficient, data-driven pharmaceutical research and development [1–3].”
- Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design (2023, background): “This enables the prediction of the activity, potency, or toxicity of novel compounds, aiding in drug discovery and design [38].”