Join Alix Lacoste VP Data Science and Michelle Gill, Senior Data Scientist at BenevolentAI for a presentation on "Machine Learning for Target Identification and Lead Optimization in Drug Discovery".
The talk will be held at JLABS@NYC, starting at 6pm. The networking will be a separate event held and hosted by NYAGIM at a nearby location, starting at 7pm.
A key challenge in the drug discovery process is the identification of potential therapeutic targets for a given disease. This process requires selecting both a target and an entity, often a compound, for modulating the target’s activity to validate its association with a disease. An additional complication is that heretofore unknown targets may be more challenging to identify but offer increased opportunities for the development of novel drugs. Once a target is validated, a compound must be identified which modulates the target in the desired fashion and has desirable properties, such as solubility and low toxicity. At BenevolentAI, we use machine learning throughout the entire process. Relation extraction models drive our unstructured pipeline. In conjunction with structured data and our own experimental results, our processing pipeline ingests information from scientific publications, abstracts, and patents. Next, this biological knowledge graph is leveraged to form predictions using relational inference algorithms, including matrix factorisation and graph convolutional models. These predictions are aggregated together with models built on genomics data and information mined from text. Druggability, tissue specificity, and other metadata are then added to surface the most promising targets to test in the lab. Models trained on chemistry data sources are able to predict relevant physicochemical properties, as well as automatically design molecules and predict synthesis pathways.
This talk will describe our process and highlight some of our early successes.
Alix has significant experience using data science and machine learning to advance biomedical discoveries. She holds a PhD in Molecular and Cellular Biology from Harvard University. Previously at IBM Watson Health, Alix led computational research projects in target identification and drug repurposing, most notably for Parkinson’s disease and amyotrophic lateral sclerosis, in collaboration with academic and pharma partners. At BenevolentAI, Alix connects AI and Drug Discovery groups to continuously improve the hypothesis generation pipeline.
Michelle Gill is a Senior Data Scientist at BenevolentAI, an AI healthcare company with capabilities from early discovery to late stage clinical development. At BenevolentAI, she utilizes data science and machine learning to facilitate both the target identification and lead optimization stages of drug discovery. Previously, Michelle was a deep learning consultant within NVIDIA’s Professional Services Group where she assisted clients in the pharmaceutical and materials science domains develop proof of concept deep learning pipelines. As a scientist at the National Cancer Institute, she developed software utilizing machine learning and compressed sensing algorithms. She holds a PhD in Molecular Biophysics and Biochemistry from Yale University and completed a postdoctoral research fellowship at Columbia University Medical School, where she developed and applied biophysical methods to study the function of cancer-associated enzymes. Michelle's scientific and machine learning work has been published in peer reviewed journals and covered by the press.
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