Portrait de Majdi Hassan

Majdi Hassan

Doctorat - UdeM
Superviseur⋅e principal⋅e
Dominique Beaini
Co-supervisor
Kirill Neklyudov
Sujets de recherche
Apprentissage profond
Biologie computationnelle
Découverte de médicaments
IA pour la science
Modèles génératifs
Modélisation moléculaire
Théorie de la fonctionnelle de la densité
Site web
Google Scholar
GitHub

Publications

Amortized Sampling with Transferable Normalizing Flows
Charlie B. Tan
Majdi Hassan
Leon Klein
Saifuddin Syed
Dominique Beaini
Michael M. Bronstein
Alexander Tong
Kirill Neklyudov
2025-06-11
ICML.cc/2025/Workshop/GenBio (spotlight)
openreview.net
openreview.net
Self-Refining Training for Amortized Density Functional Theory
Majdi Hassan
Cristian Gabellini
Hatem Helal
Dominique Beaini
Kirill Neklyudov
Density Functional Theory (DFT) allows for predicting all the chemical and physical properties of molecular systems from first principles by… (voir plus) finding an approximate solution to the many-body Schr\"odinger equation. However, the cost of these predictions becomes infeasible when increasing the scale of the energy evaluations, e.g., when calculating the ground-state energy for simulating molecular dynamics. Recent works have demonstrated that, for substantially large datasets of molecular conformations, Deep Learning-based models can predict the outputs of the classical DFT solvers by amortizing the corresponding optimization problems. In this paper, we propose a novel method that reduces the dependency of amortized DFT solvers on large pre-collected datasets by introducing a self-refining training strategy. Namely, we propose an efficient method that simultaneously trains a deep-learning model to predict the DFT outputs and samples molecular conformations that are used as training data for the model. We derive our method as a minimization of the variational upper bound on the KL-divergence measuring the discrepancy between the generated samples and the target Boltzmann distribution defined by the ground state energy. To demonstrate the utility of the proposed scheme, we perform an extensive empirical study comparing it with the models trained on the pre-collected datasets. Finally, we open-source our implementation of the proposed algorithm, optimized with asynchronous training and sampling stages, which enables simultaneous sampling and training. Code is available at https://github.com/majhas/self-refining-dft.
2025-06-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Self-Refining Training for Amortized Density Functional Theory
Majdi Hassan
Cristian Gabellini
Hatem Helal
Dominique Beaini
Kirill Neklyudov
Density Functional Theory (DFT) allows for predicting all the chemical and physical properties of molecular systems from first principles by… (voir plus) finding an approximate solution to the many-body Schrödinger equation. However, the cost of these predictions becomes infeasible when increasing the scale of the energy evaluations, e.g., when calculating the ground-state energy for simulating molecular dynamics. Recent works have demonstrated that, for substantially large datasets of molecular conformations, Deep Learning-based models can predict the outputs of the classical DFT solvers by amortizing the corresponding optimization problems. In this paper, we propose a novel method that reduces the dependency of amortized DFT solvers on large pre-collected datasets by introducing a self-refining training strategy. Namely, we propose an efficient method that simultaneously trains a deep-learning model to predict the DFT outputs and samples molecular conformations that are used as training data for the model. We derive our method as a minimization of the variational upper bound on the KL-divergence measuring the discrepancy between the generated samples and the target Boltzmann distribution defined by the ground state energy. To demonstrate the utility of the proposed scheme, we perform an extensive empirical study comparing it with the models trained on the pre-collected datasets. Finally, we open-source our implementation of the proposed algorithm, optimized with asynchronous training and sampling stages, which enables simultaneous sampling and training. Code is available at https://github.com/majhas/self-refining-dft.
2025-06-01
arXiv (publié)
doi.org
arxiv.org
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
Majdi Hassan
Nikhil Shenoy
Jungyoon Lee
Hannes Stärk
Stephan Thaler
Dominique Beaini
Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery.… (voir plus) Existing state- of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to gen- erate initial structures and diffuse over torsion angles. In this work, we introduce Equivariant Transformer Flow (ET-Flow). We showcase that a well-designed flow matching approach with equivariance and harmonic prior alleviates the need for complex internal geometry calculations and large architectures, contrary to the prevailing methods in the field. Our approach results in a straightforward and scalable method that directly operates on all-atom coordinates with minimal assumptions. With the advantages of equivariance and flow matching, ET-Flow significantly increases the precision and physical validity of the generated con- formers, while being a lighter model and faster at inference. Code is available https://github.com/shenoynikhil/ETFlow.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Equivariant Flow Matching for Molecular Conformer Generation
Majdi Hassan
Nikhil Shenoy
Jungyoon Lee
Hannes Stärk
Stephan Thaler
Dominique Beaini
2024-06-17
ICML.cc/2024/Workshop/ML4LMS (poster)
openreview.net
openreview.net