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AI in Chemical Biology: New Frontiers

WEBINAR

Only

AI in Chemical Biology: New Frontiers

Wednesday, March 17, 2021, 11:00 AM - 5:00 PM EDT

Presented By

Chemical Biology Discussion Group

The New York Academy of Sciences

 

Artificial Intelligence (AI) has the potential to enhance our understanding of chemical biology – with broad applications that include predicting reaction chemistry, automating lab processes, detangling complex biochemical processes, and hastening drug discovery and development. With rapid developments over the last decade in machine learning, natural language processing and other aspects of AI, we are at the precipice of a new era in this field, however appropriately integrating these methodologies into chemical biology research can be a daunting. To address these challenges, this one day symposium will showcase recent advances in chemical biology that were enabled by AI and highlight best practices for employing AI techniques in this field.

Registration

Member
$30
Nonmember Academia, Faculty, etc.
$65
Nonmember Corporate, Other
$85
Nonmember Not for Profit
$65
Nonmember Student, Undergrad, Grad, Fellow
$45
Member Student, Post-Doc, Fellow
$15

Keynote Speaker

James Collins, PhD
James Collins, PhD

Massachusetts Institute of Technology

Speakers

Alán Aspuru-Guzik, PhD
Alán Aspuru-Guzik, PhD

University of Toronto

Georgia McGaughey
Georgia McGaughey, PhD

Vertex Pharmaceuticals

Timothy Cernak, PhD
Timothy Cernak, PhD

University of Michigan

Joseph (Joey) Davis
Joseph (Joey) Davis, PhD

Massachusetts Institute of Technology

Debora Marks, PhD
Debora Marks, PhD

Harvard Medical School

César de la Fuente, PhD
César de la Fuente, PhD

University of Pennsylvania

Peter Madrid, PhD
Peter Madrid, PhD

SRI International

Scientific Organizing Committee

Nozomi Ando, PhD
Nozomi Ando, PhD

Cornell University

César de la Fuente, PhD
César de la Fuente, PhD

University of Pennsylvania

Sara Donnelly, PhD
Sara Donnelly, PhD

The New York Academy of Sciences

Sonya Dougal, PhD
Sonya Dougal, PhD

The New York Academy of Sciences

Wednesday

March 17, 2021

11:00 AM

Introduction and Welcome Remarks

Speaker

Barbara Knappmeyer, PhD
New York Academy of Sciences
11:10 AM

Keynote: A Deep Learning Approach to Antibiotic Discovery

Speaker

James Collins, PhD
Massachusetts Institute of Technology
11:50 AM

AI to Automate Design-Make-Test Cycles

Speaker

Peter Madrid, PhD
SRI International
12:20 PM

Break

12:30 PM

Machine Learning and Automation for Molecular and Materials Design

Speaker

Álan Aspuru-Guzik, PhD
University of Toronto
1:00 PM

Short Talk to be Selected from Submitted Abstracts

Speaker

Roy Nassar, MSc
Stony Brook University

Molecular dynamics (MD) simulations possess the ability to model biomolecular structures with atomic-scale resolution. A major bottleneck, however, is the vast configurational space that a protein can sample when folding. To alleviate this burden, we use our MD accelerator, MELD, which leverages external information to reduce the conformational problem. MELD accelerated MD (MELDxMD) complements bioinformatics, AI, and experimental approaches by integrating data from all of them into a technique that delivers high resolution structures with free energy scoring. Here, we employ MELDxMD with inferred contacts between amino acids (aa) from machine learning, to generate accurate protein structures given their aa sequence. Specifically, our protocol inputs the contacts from trRosetta—a state-of-the-art machine learning public server—as distance restraints to quickly guide the MELDxMD simulations to stable states. Importantly, the simulations successfully identify many of these native states as the lowest free energy conformations on the energy landscape, an attribute only accessible to physics-based methods. Furthermore, the formulated simulations allow modeling of proteins that surpass the upper 100 aa size limit of MD folding, as evident by accurate conformations of proteins at the maximum single-domain range (150-200aa). Overall, the work highlights the power of integrating AI with chemical biology techniques to tackle problems previously unattainable to conventional methods.

1:15 PM

Short Talk to be Selected from Submitted Abstracts

1:30 PM

Networking Break and Virtual Poster Session

2:30 PM

Design of Biological Sequences with Neural Machines

Speaker

Debora Marks, PhD
Harvard Medical School

What can we do with billions of genomes and immune repertoire sequences? We now an amazing opportunity to develop machine learning methods that can exploit this enormous natural diversity to predict the effects of human genetic variation, to predict how viral genomes may evolve and to design biological sequences for therapeutics and biotechnology. I will demonstrate how unsupervised probabilistic generative modeling of sequences can give surprisingly direct answers to questions about 3D structures, dynamics, genetic variation and the design of biological molecules optimized for specific functions. I will end by introducing challenges for extending these methods to diverse applications to a broad range of biotechnology applications.

3:00 PM

Visualizing Molecular Machines in Motion using Cryo Electron Microscopy and Deep Learning

Speaker

Joseph (Joey) Davis, PhD
Massachusetts Institute of Technology
3:30 PM

Antibiotic Discovery by Means of Computers

Speaker

César de la Fuente, PhD
University of Pennsylvania

Machines have the potential to outperform humans and revolutionize our world. In this talk, I will describe our efforts to use machines to develop computational approaches for antibiotic discovery, as well as low-cost rapid diagnostics. Computers can already be programmed for superhuman pattern recognition of images and text. In order for machines to discover novel antibiotics, they have to first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize antimicrobial activity. Said differently, machines need to be able to understand, read, write, and eventually create new molecules. I will discuss how we trained a computer to execute a fitness function following a Darwinian algorithm of evolution to select for molecular structures that interact with bacterial membranes, yielding the first artificial antimicrobials that kill bacteria both in vitro and in relevant animal models. My lab has also developed pattern recognition algorithms to mine the human proteome, identifying throughout the body thousands of antibiotics encoded in proteins with unrelated biological function, and has applied computational tools to successfully reprogram venoms into novel antimicrobials. I will also describe the development of diagnostic biosensors for COVID-19, further substantiating the exciting potential of machine biology. Computer-generated designs and innovations at the intersection between machines and biology may help to replenish our arsenal of effective drugs and generate novel diagnostics, providing much needed solutions to global health problems caused by infectious diseases.

4:00 PM

Break

4:10 PM

Synthesis in the Chemical Space Age

Speaker

Timothy Cernak, PhD
University of Michigan

The invention of a medicine typically requires multiple rounds of design and synthesis to achieve a desired balance of properties. The structure of a molecule is intimately linked to its properties, and in turn, the reactions used to make the molecule determine structure. We have been exploring the relationship of chemical reactions with physicochemical properties. Our aim is to develop an understanding of the impact that a synthetic transformation has on a molecule’s properties, and ultimately, to invent new reactions which offer complementary property profiles to the workhorse reactions from the synthetic chemists’ toolbox. To realize our vision, a team of synthetic chemists and computer scientists has been assembled, and together we develop robotics and algorithms for synthesis. We will detail some of the software and hardware we have developed to execute many synthetic experiments simultaneously. Both classic HTE in small glass shell vials, and ultraHTE in 1,536 microtiter plates are routinely applied in our research. New reactions under investigation in our laboratories will be shared, along with the application of new and existing reactions within a chemoinformatic system we have developed to tie synthetic transformations to molecular properties.

4:40 PM

AI in Chemical Biology: A Perspective from Industry

Speaker

Georgia McGaughey, PhD
Vertex Pharmaceuticals
5:10 PM

Closing Remarks

5:15 PM

Adjourn