AI and ML for Small Molecule Drug Discovery in the Big Data Era

Advancements in the informatics and omics based technologies have enhanced our ability to generate data at lower costs. The recent emergence of ‘big data’ in chemistry and biology has fundamentally revolutionized molecular biology and drug development paradigms. The recent availability of open data though various databases and online resources has led to simply too much information (big data) for a human being to assimilate using traditional research methods. The emergence of machine learning (ML) and artificial intelligence (AI) offers guidance to the research scientists to process, analyze and understand the data, and their extensive application appears to be the future for drug discovery. The traditional drug discovery process is very costly and lengthy with limited success probability. The chemical space is very large, a fraction of which we have explored. AI can be used to explore the chemical space and to understand the pattern of the complex big data. AI algorithms can make accurate predictions about complex systems involving the vast and unexplored space of molecules, reactions, and biological interactions. ML techniques have potential to identify new drug candidates in much less time than the conventional research. However, the field is still young, having ample scope for improvements in the accuracy of AI algorithms and the adoption of more standardized and rigorous benchmarks so that the discipline can mature and improve further. This collection on “AI and ML for Small Molecule Drug Discovery in the Big Data Era” showcases the latest developments in this field. Researchers working in this fascinating area are welcome to submit their fine work via the Editorial Manager system for consideration of inclusion in this topical issue which will be available online and continuously updated. For any query regarding this topical issue, please contact Prof. Kunal Roy, [email protected]