Richard Tseng

President
Proholistic Discovery
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Dr. Richard, Chih-Yuan Tseng is a seasoned theoretical information physicist and computational biophysicist with 15+ years of experience in both academic and industrial research and development in computational biology and pre-clinical drug discovery including aptamer design, pharmacokinetic and pharmacodynamic modeling. He has been heading the computational group of Sinoveda Canada to develop a Quantitative systems pharmacology based combination drug discovery platform and managing the team to apply the platform, pharmacokinetic modeling and simulation in several drug discovery programs including long COVID, hepatocarcinoma, and anti-aging treatment. He has published more than 30 peer reviewed journal publications, conference proceedings, and book chapters. He owns 8 patents and invention reports.
 

Speech title & Synopsis

ProHolistic Discovery: Elevating Clinical Success with Holistic Drug Discovery 

Roughly half of all small molecule therapeutics fail in clinical trials due to unsatisfactory ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties. This isn't just a scientific hurdle; it's a significant financial drain, often stemming from two key issues: current ADMET prediction methods don't fully capture the complexity of the human body, and traditional drug discovery often prioritizes target binding over a compound's real-world behavior within the body.
 
ProHolistic Discovery (PHD) is here to transform this. We offer a holistic approach that directly addresses these challenges. Our core technology leverages an advanced, inference-based machine learning platform. This platform learns from extensive FDA-approved drug data and established ADMET prediction algorithms to accurately infer ADMET properties in humans early in the discovery process. Beyond our technology, we seamlessly integrate with clients’ existing teams, bridging the gap between discovery and translational science. This collaborative model empowers our clients to identify promising drug candidates with greater confidence, leading to more efficient, cost-effective drug development and significantly higher success rates in clinical trials. We're not just predicting; we're de-risking your pipeline.