Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

• Published in: Future medicinal chemistry 2022-02, Vol.14 (4), p.245-270
• Main Authors: Kumar, Sethu Arun, Ananda Kumar, Thirumoorthy Durai, Beeraka, Narasimha M, Pujar, Gurubasavaraj Veeranna, Singh, Manisha, Narayana Akshatha, Handattu Sankara, Bhagyalalitha, Meduri
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; blackbox models; Blood-Brain Barrier - metabolism; Decision Making; Deep Learning; Drug Delivery Systems; Drug Discovery; Drug Industry; Drug Repositioning; high-quality data acquisition; Humans; Machine Learning
• ISSN: 1756-8919; 1756-8927
• DOI: 10.4155/fmc-2021-0243

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.