Join Mark Davies, VP Biomedical Informatics at BenevolentAI for a panel discussion on the use of AI to disrupt drug discovery: How to Reduce Time and Costs and Increase Throughput.
Where can Deep Learning show benefits in R&D?
Algorithms: Data distribution vs. outcome prediction
Critical need in DL for transparency and solution interpretability
Deep learning works great on image data. What about molecular data which is sparse, dirty, inconsistent? Drug Discovery must be accurate on outliers and deep learning is not so great here. What do we do?
Application of machine learning to toxicity and Pk
Application of machine learning to reduce computational time of computational chemistry
Target discovery and lead discovery require different methods
How can ML/AI approaches be used to speed up the drug discovery process: Target ID, lead optimization and clinical development
Do we have access to the right data, suitable for state of the art ML/AI approaches?
How can we recruit the best given the high level of competition across multiple sectors?
Mark is the VP Biomedical Informatics at BenevolentAI. He has a background in molecular genetics, bioinformatics (BSc University of Sussex) and computer science (MSc Birkbeck College) and has over 15 years of experience working on biomedical data representation, data analysis and application development. In 2001, he joined the London based biotechnology company Inpharmatica, where he was initially working on mining the output of the Human Genome Projects and eventually moved on to building Chemogenomics systems used by pharmaceutical companies, such as Bayer. Mark moved to the European Bioinformatics Institute (EMBL-EBI) as one of the founding members and technical lead for the ChEMBL resource - the largest open-source SAR database. Mark was also responsible for the successful transition of the SureChEMBL chemical patent system from Digital Science to the EMBL-EBI. Throughout his career Mark has published on how the use of biomedical data and technologies can improve the drug discovery process and enjoys identifying new opportunities this research space.