[AI Seminar Series] REMINDER: Seminar by Prof. Dhiman Ray, Friday June 6, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

Reminder about the AI seminar this Friday at noon. Please use the link
below if you plan to attend.
Sincerely,
V. Tsotras
-------------------------------------------

On Sun, Jun 1, 2025 at 10:27 AM Vassilis Tsotras <vtsotras at ucr.edu> wrote:

> Colleagues,
> the next AI Seminar will be on Friday June 6, 12:00-1:00pm at the MRB
> Seminar Room (1st floor). This seminar is in collaboration with the
> Bioengineering Department.
>
> **** Pizza and refreshments will be provided ****
>
> To keep track of the number of attendees, please *register* at:
> https://www.eventbrite.com/e/ai-seminar-series-tickets-1391174533479
>
> The talk will be given by Prof. Dhiman Ray, Department of Chemistry and
> Biochemistry, University of Oregon
>
> TITLE:
> "Interpretable and Data-Efficient Machine Learning for Biomolecular
> Simulations"
>
>
> ABSTRACT:
> Molecular dynamics (MD) simulations are widely used to study the
> mechanisms of biological processes at an atomistic resolution. Most
> physiological events, e.g., drug-target binding and protein folding, occur
> at timescales beyond milliseconds. But, we can simulate only up to a few
> microseconds at an affordable computational cost. Enhanced sampling
> algorithms can accelerate conformational sampling by applying an external
> biasing potential. The accuracy and efficiency of these algorithms are
> sensitive to the choice of collective variable (CV), a low-dimensional
> space along which the bias is applied. Deep neural networks can be used to
> construct CVs using a generic and system-agnostic feature space to compute
> an accurate free energy surface for complex molecular processes. However,
> their lack of interpretability and high cost of evaluation during
> trajectory propagation make NN-CVs difficult to apply to biomolecular
> processes. In addition, it often requires a large amount of training data
> to build NN-CVs that align well with the slow modes of the system, thereby
> increasing the overall computational cost. In this talk, I will describe a
> surrogate model approach to express the output of a neural network as a
> linear combination of a subset of the input descriptors. In addition to
> providing mechanistic insights due to their explainable nature, the
> surrogate model CVs exhibit negligible losses in efficiency and accuracy
> compared to the NN-CVs in reconstructing the underlying free energy
> surface. Surrogate model CVs are less expensive to evaluate compared to
> their neural network (NN) counterparts, making them suitable for enhanced
> sampling simulations of large and complex biomolecular processes. I will
> also describe a variational Koopman algorithm for data-efficient training
> of deep learning CVs using short metadynamics trajectories, sampling only
> forward transitions. I will show some preliminary applications of enhanced
> sampling methods on RNA conformational dynamics and ligand binding
>
>
> Bio:
> Dhiman grew up in Kolkata, India, and graduated from Indian Institute of
> Science Education and Research (IISER) Kolkata with an integrated BS-MS
> dual degree in Chemistry in 2018. He completed his PhD in Chemistry in 2022
> from the University of California Irvine working with Prof. Ioan
> Andricioaei. Subsequently, he held a postdoctoral position in the group of
> Prof. Michele Parrinello at the Italian Institute of Technology, Genoa,
> Italy. Since the fall of 2024 he is an assistant professor at the
> Department of Chemistry and Biochemistry, at the University of Oregon, USA.
>
> ------------------------------------
> Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents
> speakers working on cutting edge Foundational AI or apply AI in their
> research. The goal of these seminars is to inform the UCR community about
> current trends in AI research and promote collaborations between faculty in
> this emerging field. These seminars are open to interested faculty and
> graduate students. Please forward this email to other colleagues or
> graduate students in your lab that may be interested. After the seminar a
> discussion will follow for questions, open problems, ideas for possible
> collaborations etc.
>
> Sincerely,
> Vassilis Tsotras
> Professor, CSE Department
> co-Director, RAISE at UCR Institute
>
> Amit Roy-Chowdhury
> Professor, ECE Department
> co-Director, RAISE at UCR Institute
>
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