MIDAS Seminar

Generative AI in the Lab | Generative AI Coast-to-Coast Series

Xia NingProfessor, Biomedical Informatics, Computer Science and EngineeringThe Ohio State UniversityArlei SilvaAssistant Professor of Computer ScienceRice UniversityAngela Wilkins: ModeratorExecutive Director, Ken Kennedy InstituteRice University

Generative AI for Drug Discovery
Xia Ning

  • Abstract: Artificial Intelligence (AI) for drug discovery has been going far beyond predictive analysis over existing drug candidates. The recent, cutting-edge generative AI enables tremendous opportunities to generate new drug structures and peptide sequences that may not exist but exhibit better properties than any existing ones. This talk will demonstrate three studies on generative AI to 1) generate new small-molecule drug candidates, 2) identify synthetic paths for any (generated) small molecules and 3) generate new binding peptide sequences for MHC Class I proteins. Dr.Ning will present work using auto-encoder-based deep learning, graph neural networks, and deep reinforcement learning for the three studies. Overall, it will show how generative AI can help drug discovery that cannot be achieved using conventional methods.Bio: Dr. Xia Ning is a Professor in the Biomedical Informatics Department (BMI), and the Computer Science and Engineering Department, The Ohio State University. She is the Vice Chair for Diversity, Equity and Inclusion at BMI, the Section Chief of AI, Clinical Informatics and Implementation Science at BMI, and the Associate Director of Biomedical Informatics at OSU Center for Clinical and Translational Science (CCTS). She received her Ph.D. in Computer Science and Engineering from the University of Minnesota, Twin Cities, in 2012. Ning’s research is on Artificial Intelligence (AI) and Machine Learning with applications in drug discovery, health care and e-commerce. Specific applications include new molecule generation and drug candidate prioritization for drug discovery, drug repurposing for Alzheimer’s disease, cancer drug selection for precision medicine, information retrieval from electronic medical records (EMRs), and EMR analysis. Ning is a Fellow of the American Medical Informatics Association.

Generative Models for Graph Data: Challenges and Opportunities
Arlei Silva

  • Abstract: Graphs are a powerful framework for modeling complex systems, such as social, biological, communication, and infrastructure networks. Generative models for graphs have a long history in network science, starting with random graph models in the 50s. In the last few decades, network science has led to several advancements toward generating graphs that reproduce properties seen in the real-world (e.g. degree distributions, clustering). However, network science models, such as Preferential Attachment, are able to generate only the graph topology and their limited number of parameters lacked enough flexibility to capture more than a handful of properties. More recently, deep generative models for graphs have achieved promising results, learning graph models directly from data by extending ideas from computer vision to graph domains. The most successful case studies for these models have been for molecular graphs and, to a lesser extent, code generation. Still, graph generative models failed to achieve the same success as their vision and language counterparts. In this talk, we will discuss some of the key challenges for graph generative models and how modern results from language and vision, such as transformers and diffusion, can help us in addressing these challenges. In particular, we will use physical graphs (e.g. mesh discretizations) and cybersecurity to motivate new research directions on graph generative models.
  • Bio: My research focuses on developing algorithms and models for mining and learning from complex datasets, broadly defined as data science, especially for data represented as graphs/networks.
  • I’m particularly interested in problems motivated by computational social science, infrastructure, and healthcare. The tools that I apply to address these problems include machine learning, network science, graph theory, linear algebra, optimization, and statistics.
  • I got a Ph.D in Computer Science from the University of California, Santa Barbara, advised by Ambuj Singh, where I was also a postdoctoral scholar. Before that, I got a B.Sc and M.Sc degrees in Computer Science from Universidade Federal de Minas Gerais, in Brazil, advised by Wagner Meira Jr. I’ve also been a visiting scholar at the Rensselaer Polytechnic Institute, hosted by Mohammed J. Zaki.