Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry
Abstract
and Generation of Novel Chemistry T productivity of the pharmaceutical industry is on the decline. Failure rates in clinical trials exceed 90% after therapies are tested in model organisms, and the cost to develop a new drug exceeds $2.6 billion. Recent advances in artificial intelligence (AI) may help to reverse this trend and accelerate and improve pharmaceutical R&D. While the term AI and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and new frameworks for implementing deep neural networks (DNNs) resulted in an unprecedented acceleration of the field. Since 2014, DNNs have surpassed human accuracy in image, voice and text recognition, autonomous driving, and many other tasks. Early presentations to the pharmaceutical industry on the advances in deep learning in 2014 and 2015 resulted in skepticism and were discarded. In 2017, many pharmaceutical companies started partnering with AI startups and academics or started internal R&D programs. From training DNNs on transcriptional response data for predicting the pharmacological properties of small molecules and biomarker development, to the generation of novel chemistry, deep learning techniques rapidly propagated into many areas of biomedical research. The body of knowledge and the range of applications of deep learning and other machine learning techniques has expanded quickly and permeated into many areas of drug…
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