Recent advances in machine learning have made significant progress in fields such as computer vision, speech recognition, and natural language processing. Machine learning is, however, yet to have a major impact on drug discovery and development. It is not surprising that the progress is slow because drug development is a complex problem, spanning target identification, lead discovery and optimization, pre-clinical studies, and clinical trials. In contrast to computer vision and speech recognition, machine learning experts also do not have a good understanding of the problem setting and there is generally a lack of well curated benchmark datasets that would drive the state-of-the-art across different stages. All of this leads to the underutilization of machine learning techniques in drug development. This panel brings together distinguished academics in machine learning and accomplished leaders in the pharmaceutical industry working across different stages of drug discovery and development pipelines to discuss the challenges and opportunities from their own perspectives. The aim of the panel is to draw a contrast between different views and discover potential misalignments; thus, identifying opportunities for collaboration and driving the research while optimizing for the impact on the drug discovery and development pipelines.
Are there going to be publicly available pharma-specific ImageNet/CIFAR/MNIST/TIMIT benchmark datasets? Are machine learning labs focusing on the problems that could make a difference in terms of costs, effectiveness, and intelligence augmentation? What challenges should be prioritized to drive the field forward and utilize modern machine learning tools and models? How do improve synergies between the two fields that could be mutually beneficial?