Overview
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Explore the intersection of artificial intelligence and chemistry in this comprehensive talk by Pratyush Tiwary. Delve into the challenges and opportunities of integrating AI with theoretical and simulation methods in chemistry for new discoveries. Learn about innovative approaches like the Past-future Information Bottleneck, Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE), and Denoising Diffusion Probabilistic Models (DDPM). Discover how these techniques are applied to complex molecular systems, including protein kinases, riboswitches, and crystal polymorph nucleation. Gain insights into the potential of "Artificial Chemical Intelligence" for enabling smart molecular discovery and pushing the boundaries of computational chemistry.
Syllabus
- Intro
- Overview of Grand Challenges in the Field
- AI + Statistical Mechanics + Molecular Simulations
- Molecular Dynamics - Powerful but Limited
- Past-future Information Bottleneck
- Reweighted Autoencoded Variational Bayes for Enhanced Sampling RAVE and Applications
- Replica Exchange Molecular Dynamics REMD
- Denoising Diffusion Probabilistic Models DDPM
- Replica Exchange at Home
- Conclusion
- Q+A
Taught by
Valence Labs
Reviews
3.5 rating, based on 2 Class Central reviews
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This course highlights the role of artificial intelligence in chemistry. AI has precipitated a wide range of predictions and impacts science and mainly chemistry research. It has paved the way for molecular structure prediction, mainly in drug designing. Despite these breakthroughs, challenges persist regarding data sparsity, bias, model interpretability, and integration with experimental workflows.
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Hard to follow and difficult to understand. Could use captions to help people understand what he is saying as he speaks very fast and not very clearly. Course notes could also help with understanding what is going on.