NeurIPS 2023 Workshop AI4D3 Submissions

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
Xiangzhe Kong, Wenbing Huang, Yang Liu
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
TrustAffinity: accurate, reliable and scalable out-of-distribution protein-ligand binding affinity prediction using trustworthy deep learning
Amitesh Badkul, Li Xie, Shuo Zhang, Lei Xie
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Multitask-Guided Self-Supervised Tabular Learning for Patient-Specific Survival Prediction
You Wu, Omid Bazgir, Yongju Lee, Tommaso Biancalani, James Lu, Ehsan Hajiramezanali
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Explaining Drug Repositioning: A Case-Based Reasoning Graph Neural Network Approach
Adriana Carolina Gonzalez Cavazos, Roger Tu, Meghamala Sinha, Andrew Su
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility
Michael Plainer, Marcella Toth, Simon Dobers, Hannes Stark, Gabriele Corso, Céline Marquet, Regina Barzilay
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence
Shuo Zhang, Lei Xie
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction
Weitong Zhang, Xiaoyun Wang, Weili Nie, Joe Eaton, Brad Rees, Quanquan Gu
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang, Bozitao Zhong, Zuobai Zhang, Narendra Chaudhary, Sanchit Misra, Jian Tang
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
PiNUI: A Dataset of Protein-Protein Interactions for Machine Learning
Geoffroy Dubourg-Felonneau, Eyal Akiva, Daniel Wesego, Ranjani Varadan
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Graph Neural Bayesian Optimization for Virtual Screening
Miles Wang-Henderson, Bartu Soyuer, Parnian Kassraie, Andreas Krause, Ilija Bogunovic
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Compositional Deep Probabilistic Models of DNA Encoded Libraries
Benson Chen, Mohammad Sultan, Theofanis Karaletsos
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
FragXsiteDTI: an interpretable transformer-based model for drug-target interaction prediction
Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Sina Abdidizaji, Ozlem Garibay
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
Seonghwan Seo, Woo Youn Kim
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
CryoSTAR: Cryo-EM Heterogeneous Reconstruction of Atomic Models with Structural Regularization
Yi Zhou, Yilai Li, Jing Yuan, Fei YE, Quanquan Gu
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Do chemical language models provide a better compound representation?
Mirko Torrisi, Saeid Asadollahi, Antonio De la Vega de Leon, Kai Wang, Wilbert Copeland
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
CongFu: Conditional Graph Fusion for Drug Synergy Prediction
Oleksii Tsepa, Bohdan Naida, Anna Goldenberg, BO WANG
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
TopoPool: An Adaptive Graph Pooling Layer for Extracting Molecular and Protein Substructures
Mattson Thieme, Majdi Hassan, Chetan Rupakheti, Kedar Balaji Thiagarajan, Abhishek Pandey, Han Liu
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Gotta be SAFE: A New Framework for Molecular Design
Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan Siu Chi Lim, Prudencio Tossou
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms
Bowen Jing, Tommi Jaakkola, Bonnie Berger
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Data-Efficient Molecular Generation with Hierarchical Textual Inversion
Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening
Zhonglin Cao, Simone Sciabola, Ye Wang
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Machine Learning Guided AQFEP: A Fast & Efficient Absolute Free Energy Perturbation Solution for Virtual Screening
Jordan Crivelli-Decker, Zane Beckwith, Gary Tom, Ly Le, Sheenam Khuttan, Romelia Salomon-Ferrer, Jackson Beall, Andrea Bortolato
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone
Leap: molecular synthesisability scoring with intermediates
Antonia Calvi, Théophile Gaudin, Dominik Miketa, Dominique Sydow, Liam Wilbraham
- Published: 25 Oct 2023, Last Modified: 10 Dec 2023
- AI4D3 2023 Poster
- Readers: Everyone