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Accepted Posters
The following table displays the accepted posters and which poster session they will be part of. There will be two poster sessions: Session I is from 11am - 12pm and Session II is from 2:30pm to 3:30pm. Presenters are expected to be present for the duration of the session their poster is being presented in.
Session I and Session II posters are not arranged in numerical order. Please scroll to see if your poster is part of Session I or Session II.
Poster Format
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Posters should be sized 48” x 36”, and oriented in landscape.
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All posters should be physically printed. No screens will be available for electronic posters.
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Posters should be printed and brought to the conference on October 21, 2022. Please do not mount your poster on foam core. We will provide push pins for you to affix your poster to the poster board.
Poster Session
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Format. Poster presentations will take place in 2 sessions, each 60 minutes long. The first will run from 11:00 AM to 12:00 PM, and the second will run from 2:30 PM to 3:30 PM.
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Location. The poster session will take place on the 1st floor of the Koch Institute in the main hallway and in Luria Auditorium.
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Schedule and Map. Board numbers and board assignments will be provided day-of at the Registration Table.
Last updated: Oct. 20, 2022
Title | Authors | Session |
|---|---|---|
Generative MD Simulation of Polymer Chains with RL, a.k.a. P4 | Paloma Gonzalez-Rojas, Gregory Rutledge | 1 |
Challenging the i.i.d. assumption in molecular scoring | Cas Wognum, Prudencio Tossou, Emmanuel Noutahi | 1 |
General, Powerful, Scalable Graph Transformer for molecules | Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini | 2 |
Multi-Objective GFlowNets | Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio | 1 |
Robust Molecular Structure Recognition with Image-To-Graph Generation | Yujie Qian, Jiang Guo, Zhengkai Tu, Connor W. Coley, Regina Barzilay | 2 |
Predicting the Targets of T-cells | Jeremy Wohlwend, Nitan Shalon, Regina Barzilay | 2 |
Torsional Diffusion for Molecular Conformer Generation | Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola | 2 |
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction | Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi Jaakkola | 2 |
Neural molecular evolution model for protein mutations prediction | Wenxian Shi, Nitan Shalon, Regina Barzilay | 1 |
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement | Wengong Jin, Regina Barzilay, Tommi Jaakkola | 1 |
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking | Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola | 1 |
Chemical Structure Elucidation with MS/MS | Serena Khoo, Regina Barzilay | 1 |
Modeling metabolic networks for Molecular Property Prediction | Itamar Chinn, Peter Mikhael, Regina Barzilay | 1 |
Sign and Basis Invariant Networks for Spectral Graph Representation Learning | Derek Lim*, Joshua Robinson*, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka | 1 |
Scalable metabolite annotation with chemical formula transformers | Samuel Goldman, Jeremy Wohlwend, Martin Strazar, Guy Haroush, Connor W. Coley | 1 |
De novo functional groups designed to enhance neuronal integrin α5β1 binding using deep reinforcement learning | Isuru S. Herath, Jingjie Yeo | 1 |
GNINA 1.0: molecular docking with deep learning | Andrew T. McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri & David Ryan Koes | 1 |
Atomic and Bond Properties Prediction by Message Passing Neural Networks | Shih-Cheng Li, Haoyang Wu, Charles McGill, Hao-Wei Pang, William H. Green | 1 |
Data-efficient Molecular Property Prediction Using Graph Grammars as Intrinsic Geometry | Minghao Guo, Veronika Thost, Samuel Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik | 1 |
GNN-MS: predicting mass spectra of small molecules using graph neural networks and formula vocabularies | Michael Murphy, Joe Davison, Gennady Voronov, Tobias Kind, Tom Butler | 1 |
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design | Keir Adams, Connor W. Coley | 1 |
Studying RNA Binding Proteins with Machine Learning | Felix Faltings, Regina Barzilay, Tommi Jaakkola | 1 |
Calculating Reaction and Activation Free Energies Rigorously | Johannes C. B. Dietschreit, Dennis J. Diestler, Christian Ochsenfeld, Rafael Gómez-Bombarelli | 1 |
Latent Denoising for Scoring Protein Complexes | Menghua Wu, Wengong Jin, Regina Barzilay, Tommi Jaakkola | 1 |
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization | Wenhao Gao, Tianfan Fu, Jimeng Sun, Connor W. Coley | 2 |
Pre-training via Denoising for Molecular Property Prediction | Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin | 2 |
Ligand-aware protein sequence design using protein self contacts | Jody Mou, Benjamin Fry, Nicholas Polizzi | 2 |
Design of Peptide-Guided Protein Degraders with Sequence-Based Language Models | Garyk Brixi, Kalyan Palepu, Suhaas Bhat, Pranam Chatterjee | 2 |
Flexible Backbone Construction via Deep Generative Model for de novo Protein Design | Boqiao Lai | 2 |
Generative diffusion models of 3D protein structure | Jason Yim, Brian Trippe, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, Tommi Jaakkola | 2 |
Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction | Yuning You, Yang Shen | 2 |
Hit Expansion Made Easy with Transformer Models | Emma P. Tysinger, Vishnu Sresht, Brajesh K. Rai, Anton V. Sinitskiy | 2 |
Modeling substrate turnover dynamics to guide the redesign of a natural enzyme for increased activity | Elijah Karvelis, Bruce Tidor | 2 |
NequIP and Allegro: E(3)-equivariant architectures for accurate, sample-efficient, and scalable interatomic potentials | Albert Musaelian, Simon L. Batzner, Boris Kozinsky | 2 |
Pre-training via Denoising for Molecular Property Prediction | Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin | 2 |
End-to-end differentiable construction of molecular mechanics force fields | Yuanqing Wang, Josh Fass, Benjamin Kaminow,
John E. Herr, Dominic Rufa, Ivy Zhang,
Iván Pulido, Mike Henry, Hannah Bruce MacDonald, Kenichiro Takaba, John D. Chodera | 2 |
PREDICTIING ANTI-VIRAL DRUGs WITH MODIFIED LSTM & COMPARING EFFECTIVENESS WITH STATE-OF-THE-ART PREDICTED DRUGs | Md.Sadek Hoosain Asif | 2 |
Multiscale Characterization of Graphene Fracture using Machine Learning & Empirical Interatomic Potentials and Mechanics | Hanfeng Zhai, Jingjie Yeo | 2 |
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