Deep learning in drug discovery: opportunities, challenges and future prospects

• Published in: Drug discovery today 2019-10, Vol.24 (10), p.2017-2032
• Main Author: Lavecchia, Antonio
• Format: Article
• Language: English
• Subjects: Deep Learning; Drug Discovery - methods; Forecasting; Humans
• ISSN: 1359-6446; 1878-5832
• DOI: 10.1016/j.drudis.2019.07.006

•Deep learning methods have gained outstanding achievements.•We review deep learning methods/tools relevant to drug discovery research.•We discuss opportunities, challenges and advantages of methods and tools.•The wide applicability of the approaches is demonstrated in several case studies.•Future prospects of deep learning in drug discovery are discussed. Artificial Intelligence (AI) is an area of computer science that simulates the structures and operating principles of the human brain. Machine learning (ML) belongs to the area of AI and endeavors to develop models from exposure to training data. Deep Learning (DL) is another subset of AI, where models represent geometric transformations over many different layers. This technology has shown tremendous potential in areas such as computer vision, speech recognition and natural language processing. More recently, DL has also been successfully applied in drug discovery. Here, I analyze several relevant DL applications and case studies, providing a detailed view of the current state-of-the-art in drug discovery and highlighting not only the problematic issues, but also the successes and opportunities for further advances. This paper focuses on deep learning approaches in the context of drug discovery for designing new effective molecules, predicting for the desired molecular property profiles and planning synthesis.