name: siam-jul25 class: title, middle ### Towards equilibrium molecular conformation generation with GFlowNets .highlight2[Alexandra Volokhova] & .highlight2[Léna Néhale-Ezzine] .smaller[Michał Koziarski, Piotr Gaiński, Cheng-Hao Liu, Luca Scimeca, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Emmanuel Bengio, Prudencio Tossou, Alán Aspuru-Guzik, Yoshua Bengio] Presenting: Alex Hernández-García (he/il/él) .turquoise[[SIAM 2025 · Computational Data Science of Nanostructures](https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=84791) · Montreal · July 28th 2025] .center[ <a href="https://mila.quebec/"><img src="../assets/images/slides/logos/mila-beige.png" alt="Mila" style="height: 4em"></a> <a href="https://www.umontreal.ca/"><img src="../assets/images/slides/logos/udem-white.png" alt="UdeM" style="height: 4em"></a> ] .center[ <a href="https://institut-courtois.umontreal.ca/"><img src="../assets/images/slides/logos/institut-courtois.png" alt="Institut Courtois" style="height: 2.5em"></a> <a href="https://ivado.ca/"><img src="../assets/images/slides/logos/ivado.png" alt="IVADO" style="height: 2.5em"></a> ] .smaller[.footer[ Slides: [alexhernandezgarcia.github.io/slides/{{ name }}](https://alexhernandezgarcia.github.io/slides/{{ name }}) ]] .qrcode[] --- count: false name: title class: title, middle ### Motivation: facilitating scientific discoveries .center[] --- ## Why scientific discoveries? .context[Climate change is a major challenge for humanity.] <br><br> .center[ <figure> <img src="../assets/images/slides/climatechange/ipcc_warming.png" alt="Observed (1900–2020) and projected (2021–2100) changes in global surface temperature (relative to 1850–1900)" style="width: 100%"> <figcaption>.smaller[Observed (1900–2020) and projected (2021–2100) changes in global surface temperature relative to 1850–1900 (adapted from: <a href="https://www.ipcc.ch/report/sixth-assessment-report-cycle/">IPCC Sixth Assessment Report</a>)]</figcaption> </figure> ] .conclusion["The evidence is clear: the time for action is now." .smaller[IPCC Report, 2022]] --- ## Why scientific discoveries? .context[Climate change is a major challenge for humanity.] .center[ <figure> <img src="../assets/images/slides/climatechange/who_climate_health.png" alt="Climate change presents a fundamental threat to human health." style="width: 100%"> <figcaption>.smaller[Climate-sensitive health risks (adapted from: <a href="https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health">World Health Organization</a>)]</figcaption> </figure> ] .smaller[ * Environmental factors take the lives of around 13 million people _per year_. * Climate change affects people’s mental and physical health, access to clean air, safe water, food and health care. ] .full-width[ .conclusion["Climate change is the single biggest health threat facing humanity." .smaller[[WHO and WMO](https://climahealth.info/), 2024]] ] --- ## Why scientific discoveries? ### The potential of materials discovery .context["The time for action is now"] -- > "Limiting global warming will require major transitions in the energy sector. This will involve a substantial reduction in fossil fuel use, widespread electrification, .highlight1[improved energy efficiency, and use of alternative fuels (such as hydrogen)]." .cite[IPCC Sixth Assessment Report, 2022] > "Reducing industry emissions will entail coordinated action throughout value chains to promote all mitigation options, including demand management, .highlight1[energy and materials efficiency, circular material flows]." .cite[IPCC Sixth Assessment Report, 2022] -- <br> .conclusion[Mitigation of the climate crisis requires innovation in the materials sector.] ??? Antimicrobial resistance - https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance - https://www.who.int/news-room/feature-stories/detail/donors-making-a-difference--climate-change-and-its-impact-on-health - https://www.who.int/news/item/31-10-2022-who-and-wmo-launch-a-new-knowledge-platform-for-climate-and-health - https://www.who.int/news/item/08-02-2024-who-medically-important-antimicrobial-list-2024 - https://cdn.who.int/media/docs/default-source/gcp/who-mia-list-2024-lv.pdf?sfvrsn=3320dd3d_2 - https://www.who.int/publications/i/item/9789240047655 --- ## Why scientific discoveries? ### The potential of drug discovery .context[Drug discovery and vaccine development play a crucial role in modern healthcare systems.] .right-column-33[ .center[] ] --- count: false ## Why scientific discoveries? ### The potential of drug discovery .context[Drug discovery and vaccine development play a crucial role in modern healthcare systems.] .right-column-33[ .center[] ] .left-column-66[ .highlight1[Bacterial antimicrobial resistance] contributed to 4.95 million deaths in 2019. .cite[World Health Organisation (WHO), 2023] WHO's latest annual review identified 27 antibiotics in clinical development that address WHO bacterial priority pathogens, of which .highlight1[only 6 were classified as innovative]. "The recently approved antibacterial agents are .highlight1[insufficient to tackle the challenge] of increasing emergence and spread of antimicrobial resistance". .cite[World Health Organisation (WHO), 2021] ] --- count: false ## Why scientific discoveries? ### The potential of drug discovery .context[Drug discovery and vaccine development play a crucial role in modern healthcare systems.] .right-column-33[ .center[ <figure> <img src="../assets/images/slides/drugs/who_notimetowait.png" alt="No time to wait" style="width: 55%"> <figcaption><small>"No time to wait". Source: <a href="https://www.who.int/docs/default-source/documents/no-time-to-wait-securing-the-future-from-drug-resistant-infections-en.pdf">WHO</a>.</small></figcaption> </figure> ] ] .left-column-66[ .highlight1[Bacterial antimicrobial resistance] contributed to 4.95 million deaths in 2019. .cite[World Health Organisation (WHO), 2023] WHO's latest annual review identified 27 antibiotics in clinical development that address WHO bacterial priority pathogens, of which .highlight1[only 6 were classified as innovative]. "The recently approved antibacterial agents are .highlight1[insufficient to tackle the challenge] of increasing emergence and spread of antimicrobial resistance". .cite[World Health Organisation (WHO), 2021] ] .full-width[ .conclusion["No time to wait". Alongside other necessary actions, drug discovery plays a key role in tackling the antimicrobial resistance global threat.] ] --- ## Machine Learning for Science .center[] .conclusion[Machine learning research has the potential to facilitate scientific discoveries to tackle climate and health challenges.] --- count: false ## Machine Learning for Science and Science for Machine Learning .center[] .conclusion[Machine learning research has the potential to facilitate scientific discoveries to tackle climate and health challenges. Scientific challenges stimulate in turn machine learning research.] --- ## Sampling molecular conformations Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. Goal: given a molecular graph $G$, to sample conformations from the Boltzmann distribution, as determined by the molecule's energy. -- .right-column[.center[]] .left-column[ We represent molecules by their intrinsic properties: - Torsion angles - Bond lengths - Bond angles ] --- count: false ## Sampling molecular conformations Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. Goal: given a molecular graph $G$, to sample conformations from the Boltzmann distribution, as determined by the molecule's energy. .right-column[.center[]] .left-column[ We represent molecules by their intrinsic properties: - .highlight1[Torsion angles]: responsible for most of the variance in the conformational space - Bond lengths: considered constant (local structure) - Bond angles: considered constant (local structure) ] --- ## Sampling molecular conformations _with GFlowNets_ GFlowNets are amortised samplers designed to sample proportionally to a reward function. Here, we used the molecular energy as reward function: Semi-empirical (GFN2-xTB), force field (GFN-FF), neural network potential (TorchANI). -- <figure> <img src="../assets/images/slides/conformers/schematic.png" alt="Torsional-GFN v1" style="width: 100%"> </figure> 1. Input: a small molecule represented as a SMILES. 2. Obtain molecular graph and _local structure_ from RDKit. 3. Train a GFlowNet to sample adjustments of the torsion angles of the molecular graph. --- ## Results: 2D experiments .center[] .conclusion[GFlowNet nearly perfectly learns to sample proportionally to the energy distribution in molecules with two torsion angles.] --- ## Results: multiple torsion angles .center[] --- count: false ## Results: multiple torsion angles .center[] .conclusion[GFlowNet can also learn the energy distributions over multiple torsion angles, better than relying solely on RDKit.] --- ## Preliminary conclusions - GFlowNet can sample diverse low-energy conformations of drug-like molecules with multiple torsion angles. - GFlowNet can learn various energy landscapes with different energy estimators. Paper: Volokhova, Koziarski, et al. [Towards equilibrium molecular conformation generation with GFlowNets](https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00023d). Digital Discovery (2024). -- - Since a GFlowNet needs to be trained for each molecule, is this practically useful at all? - Is MCMC not as good as GFlowNets for this task? .conclusion[Besides amortising each energy evaluation for a given molecule, GFlowNets can potentially _generalise to unseen molecules_.] --- ## Torsional-GFN ### Conditional conformation generation common to all molecules <figure> <img src="../assets/images/slides/conformers/schematic_torsionalgfn.png" alt="Torsional-GFN v2" style="width: 100%"> </figure> 1. Input: a data set of small molecules represented as a SMILES. 2. Obtain molecular graph and _local structure_ from molecular dynamics. 3. Train a GFlowNet to sample adjustments of the torsion angles of the molecular graph. 4. At sampling time, any SMILES (even from unseen molecules) can be passed as an input. --- ## Results - We trained Torsional‐GFN on a subset of 6 molecules, each with 2 rotatable torsion angles, from the FreeSolv dataset. - Local structures fixed to the values of one arbitrary conformation from the MD simulation dataset. - Energy function: MMFF94. -- .left-column[ .center[ <figure> <img src="../assets/images/slides/conformers/2d_cid_3_train.png" alt="Local structure in train set" style="width: 80%"> <figcaption><small>Local structure in train set</a>.</small></figcaption> </figure> ]] .right-column[ .center[ <figure> <img src="../assets/images/slides/conformers/2d_cid_3_unseen.png" alt="Unseen local structure" style="width: 80%"> <figcaption><small>Unseen local structure during training</a>.</small></figcaption> </figure> ]] --- ## Results ### Energy histograms .center[] --- ## Conclusions - Torsional-GFN successfully extends the idea of sampling molecular conformations with GFlowNets to a _unique, conditional_ model with potential to generalise to unseen molecules. - This development entail significant challenges, such as training a GNN as the policy architecture. - For practical use, Torsional-GFN shoudl be scaled to multiple torsional angles and many more molecules. - It may be beneficial to also sample the local structures. Pre-print: Volokhova, Nehale-Ezzine, et al. [Torsional-GFN: a conditional conformation generator for small molecules](https://arxiv.org/abs/2507.11759). arXiv:2507.11759 (2025). --- name: siam-jul25 class: title, middle .bigger[.highlight2[Alexandra Volokhova], .highlight2[Léna Néhale-Ezzine], Michał Koziarski, Piotr Gaiński, Cheng-Hao Liu, Luca Scimeca, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Emmanuel Bengio, Prudencio Tossou, Alán Aspuru-Guzik, Yoshua Bengio] .smaller[Alex Hernández-García (he/il/él)] .center[ <a href="https://mila.quebec/"><img src="../assets/images/slides/logos/mila-beige.png" alt="Mila" style="height: 3em"></a> <a href="https://www.umontreal.ca/"><img src="../assets/images/slides/logos/udem-white.png" alt="UdeM" style="height: 3em"></a> <a href="https://institut-courtois.umontreal.ca/"><img src="../assets/images/slides/logos/institut-courtois.png" alt="Institut Courtois" style="height: 3em"></a> <a href="https://ivado.ca/"><img src="../assets/images/slides/logos/ivado.png" alt="IVADO" style="height: 3em"></a> ] .footer[[alexhernandezgarcia.github.io](https://alexhernandezgarcia.github.io/) | [alex.hernandez-garcia@mila.quebec](mailto:alex.hernandez-garcia@mila.quebec)] | [alexhergar.bsky.social](https://bsky.app/profile/alexhergar.bsky.social) [](https://bsky.app/profile/alexhergar.bsky.social)<br> .smaller[.footer[ Slides: [alexhernandezgarcia.github.io/slides/{{ name }}](https://alexhernandezgarcia.github.io/slides/{{ name }}) ]]