About - Registration - Schedule - Keynote Speakers - Keynote Abstracts - Accepted Contributions - Important Dates - Call for Papers

IMPORTANT
2024-09-18: Registration for the workshop is now open. Please register and join us!

About

Machine Learning models for molecules and materials are essential drivers in drug discovery, materials design, environmental science, and precision medicine. With these models, experts are enabled to meaningfully interact with molecular systems and to design them. The interaction with molecular systems allows for deeper insights into typically complex systems and facilitate (enable) the development of new drugs, personalized medical treatments, and innovative materials - e.g., advanced fuel cells in the field of energy conversion and storage or materials for environmental remediation in the field of environmental science. Recently, large Language Models (LLMs) have demonstrated remarkable performance across various domains in machine learning, revolutionizing natural language processing tasks and showing notable capabilities in understanding and generating human-like text. LLMs have also had a profound impact on molecular machine learning because they allow for human-AI interaction, interpretability, zero- and few-shot adaption, and in-context learning. However, the application of LLM architectures to molecular data presents significant challenges. Therefore, this workshop focuses on exploring potentials of ML methods with broader capabilities like LLMs incorporate them. We welcome all work which might provide potential steps towards these broader capabilities, e.g., by tackling the areas of knowledge and interaction, adaptability and robustness, abstraction and reasoning, or efficiency.

Join us at our workshop at which experts from diverse fields, ranging from ML and LLMs to molecular sciences, will collaborate to explore new potentials of ML methods for molecules and materials in the era of LLMs.

Registration

The workshop will be open to everyone without a registration fee. You can register here!

Schedule

Fri, Dec 6th, 09:00 am - 6:00 pm, CET; venue: Fritz Haber Institute of the Max Planck Society, Berlin; online at Zoom

CET Event Speakers Title
  1. Session Session Chair: Johannes Margraf  
09:00 Opening remarks ML4Mol Chair  
09:00 Invited Talk Tim Duignan Neural network potentials solve the connecting scales problem
09:30 Invited Talk John Chodera Teaching free energy calculations to learn
10:00 Contributed talk Rıza Özçelik The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out
10:15 Contributed talk Andreas Burger DEQuify your force field: More efficient simulations using deep equilibrium models
10:30 Contributed talk Sarina Kopf Sample Efficient Goal-Directed Catalyst Design for the Morita-Baylis-Hillman Reaction
10:45 Contributed talk Sohvi Luukkonen Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
11:00 Poster Session 1 (PS 1) Poster discussion  
12:00 Break    
  2. Session Session Chair: Günter Klambauer  
13:00 Invited talk Cecilia Clementi Modeling protein dynamics with machine learning and molecular simulation
13:30 Invited talk Daniel Probst Less is More
14:00 Invited talk Igor Tetko Expectations, achievements and lessons of AIDD and AIChemist Marie Skłodowska-Curie Innovative Training Network - European Industrial Doctorate projects
14:30 Contributed talk Lars Leon Schaaf ML Force Fields for Computational NMR Spectra of Dynamic Materials across Time-Scales
14:45 Contributed talk Sara Tanovic An exploration of dataset importance in single-step retrosynthesis prediction
15:00 Contributed talk RuiKang OuYang BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
15:15 Break    
  3. Session Session Chair: Alaa Bessadok  
15:45 Invited talk Jutta Rogal Mapping phase diagrams with deep generative models
16:15 Invited talk Jian Tang Geometric Deep Learning for Protein Design
16:45 Poster Session 2 (PS 2) Poster discussion  
17:15 Retreat of ELLIS fellows    
18:00 End & Closing remarks    

Keynote Speakers

Accepted contributions (poster)

ID Title Authors
1 DEQuify your force field: More efficient simulations using deep equilibrium models Andreas Burger, Luca Thiede, Alan Aspuru-Guzik, Nandita Vijaykumar
2 ML Force Fields for Computational NMR Spectra of Dynamic Materials across Time-Scales Lars Leon Schaaf, Benjamin J. Rhodes, Mary E. Zick, Suzi M. Pugh, Jordon S. Hilliard, Shivani Sharma, Casey R. Wade, Phillip J. Milner, Gabor Csanyi, Alexander C. Forse
3 DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties Marie Oestreich, Matthias Becker
4 Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Jingyi Zhao, Yuxuan Ou, Austin Tripp, Morteza Rasoulianboroujeni, José Miguel Hernández-Lobato
5 In silico enzyme prediction and generation by fine tuning protein language models Marco Nicolini, Emanuele Saitto, Ruben Jimenez, Emanuele Cavalleri, Aldo Galeano, Dario Malchiodi, Alberto Paccanaro, Peter N Robinson, Elena Casiraghi, Giorgio Valentini
6 A language model assistant for biocatalysis Yves Gaetan Nana Teukam, Francesca Grisoni, Matteo Manica
7 Tango*: Constrained synthesis planning using chemically informed value functions Daniel P Armstrong, Zlatko Jončev, Jeff Guo, Philippe Schwaller
8 A Generative Model for the Design of Synthesizable Ionizable Lipids Yuxuan Ou, Jingyi Zhao, Austin Tripp, Morteza Rasoulianboroujeni, José Miguel Hernández-Lobato
9 The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out Rıza Özçelik, Francesca Grisoni
10 BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching RuiKang OuYang, Bo Qiang, José Miguel Hernández-Lobato
11 Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models Fengzhe Zhang, Jiajun He, Laurence Illing Midgley, Javier Antoran, José Miguel Hernández-Lobato
12 UPT++: Latent Point Set Neural Operators for Modeling System State Transitions Andreas Fürst, Florian Sestak, Artur Petrov Toshev, Benedikt Alkin, Nikolaus A. Adams, Andreas Mayr, Günter Klambauer, Johannes Brandstetter
13 Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences Niklas Schmidinger, Lisa Schneckenreiter, Philipp Seidl, Johannes Schimunek, Pieter-Jan Hoedt, Johannes Brandstetter, Andreas Mayr, Sohvi Luukkonen, Sepp Hochreiter, Günter Klambauer
14 Enhancing Molecular Property Prediction with GNNs via Architecture-Agnostic Graph Transformations Zhifei Li, Gerrit Großmann, Verena Wolf
15 Fantastic SMILES Augmentation Strategies and Where to Find Them Helena Brinkmann, Francesca Grisoni, Antoine Argante, Hugo ter Steege
16 Rectified Flow For Structure Based Drug Design Daiheng Zhang, Chengyue Gong, Qiang Liu
17 Equivariant conditional diffusion model for exploring the chemical space around Vaska’s complex François R J Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel N. Schmidt, Arghya Bhowmik
18 Sample Efficient Goal-Directed Catalyst Design for the Morita-Baylis-Hillman Reaction Sarina Kopf, Jeff Guo, Jaime Martin, Johannes Schoergenhumer, Cristina Nevado, Philippe Schwaller
19 Steering generative deep learning with task negation for de novo drug design Sarah de Ruiter, Rıza Özçelik, Francesca Grisoni
20 MØDRCN – Open-Source Chemical Reservoir Computing Tool Mehmet Aziz Yirik, Jakob Lykke Andersen, Rolf Fagerberg, Daniel Merkle
21 Enhancing Generalizability in Permeability Prediction for Macrocyclic Peptides Anna Borisova, Rebecca Manuela Neeser, Philippe Schwaller
22 An exploration of dataset importance in single-step retrosynthesis prediction Sara Tanovic, Fernanda Duarte
23 Towards transparent ML for kinase drug discovery: Insights from structural binding models Joschka Groß, Michael Backenköhler, Paula Linh Kramer, Verena Wolf, Andrea Volkamer
24 Autonomous Atomistic Simulations with Hierarchical LLM Agents Ziqi Wang, Hongshuo Huang, Hancheng Zhao, Changwen Xu, Jan Janssen, Venkatasubramanian Viswanathan
25 Protein Language Model-Driven Zoonotic Risk Assessment of SARS-CoV-2 Variants Payel Das, Alessandra Toniato, Aurelie Lozano, Vijil Chenthamarakshan
26 RxnRule: Filtering single step reaction predictions based on reaction class incompatibilities Zlatko Jončev, Daniel P Armstrong, Victor Sabanza Gil, Ziad El Malki, Philippe Schwaller
27 Physics-Aware Diffusion Models for Micro-structure Material Design Jacob K Christopher, Stephen Baek, Ferdinando Fioretto

Important dates

Call for papers

We are calling for papers exploring machine learning for molecules and materials in the era of LLMs. Topics include (but not limited to):

Please submit your contributions on OpenReview until November 1 2024 11:59 PM UTC-0. The submissions should be in PDF and follow the NeurIPS template with a maximum of 5 pages (not including references and appendices). We also welcome extended abstract submissions (2 pages). Please anonymize your paper since the review process is dual-anonymous. Filling the NeurIPS checklist with the paper is not compulsory.

Note that workshop papers often represent the current status of on-going projects. They should not be considered as final versions of record for a particular project. The workshop is non-archival.

Best Paper Award

The best paper award (1000€) is sponsored by AstraZeneca.

Two papers share the best paper award:

Award committee: Andrea Volkamer, Philippe Schwaller, Cecilia Clementi

Organizing Committee and Contact

Chairs: Johannes Margraf, Francesca Grisoni, Günter Klambauer

Organizing committee: Alaa Bessadok, Sovhi Luukkonen, Johannes Schimunek, Karsten Reuter, Giulia Glorani, Francesca Grisoni, Johannes Margraf, Günter Klambauer

AstraZeneca has provided a sponsorship grant towards this independent Programme.

Contact: ml4molecules@ml.jku.at