Institute of Information Science, Academia Sinica

Computational studies of molecules (such as through Molecular Dynamics, MD, simulations) offer a set of vital approaches for the elucidation of molecular mechanisms at the resolutions that are currently difficult or impossible to obtain from wet-lab experiments. In a similar way, the use of Artificial Intelligence (most especially, Deep Learning, DL) techniques makes it possible to study and even design and create new molecules that have specific desired properties with high success rates. The computational studies of molecules using a combination of MD simulations and DL techniques is, therefore, an active area of research. In this talk, I will introduce MD simulations, Enhanced Sampling by Accelerated MD Simulations, Free Energy Calculation by Adaptively Biased MD Simulations, MD Simulations Applications in "The Impairment of TorsinA's Binding to and Interactions With Its Activator: An Atomistic Molecular Dynamics Study of Primary Dystonia" and in "In Silico Study Reveals How E64 Approaches, Binds to, and Inhibits Falcipain-2 of Plasmodium falciparum that Causes Malaria in Humans". Furthermore, DL, Autoencoders (AE), Variational Autoencoders (VAE), Recurrent Neural Network (RNN), and the generation of Small Molecules using CharRNN and VAE will be introduced. Finally, I will introduce a model for enhanced sampling that combines MD and DL techniques -- Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE).

Dr. Emmanuel Salawu studied (Medical) Physiology in college and later earned a Masters Degree in Computer Science with Artificial Intelligence research focus. He earned his first Ph.D. in Bioinformatics and Structural Biology and his second Ph.D. in Public Health with a Specialization in Epidemiology. He has carried out research studies that span the fields of medical sciences and computational sciences. Dr. Salawu works as a Senior Research Scientist at the Taiwan R&D Office of Insilico Medicine where he serves as the Head of Deep Learning.

參考：https://docs.google.com/document/d/10OJW2eUD4pbi0BZi89bGeJHsvCV3rfWuFoGaU-xgY0U/edit