Img2Mol–accurate SMILES recognition from molecular graphical depictions DA Clevert, T Le, R Winter, F Montanari Chemical science 12 (42), 14174-14181, 2021 | 56 | 2021 |
Parameterized hypercomplex graph neural networks for graph classification T Le, M Bertolini, F Noé, DA Clevert International Conference on Artificial Neural Networks, 204-216, 2021 | 44 | 2021 |
Neuraldecipher–reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures T Le, R Winter, F Noé, DA Clevert Chemical science 11 (38), 10378-10389, 2020 | 41 | 2020 |
Unsupervised learning of group invariant and equivariant representations R Winter, M Bertolini, T Le, F Noé, DA Clevert Advances in Neural Information Processing Systems 35, 31942-31956, 2022 | 33 | 2022 |
Equivariant graph attention networks for molecular property prediction T Le, F Noé, DA Clevert arXiv preprint arXiv:2202.09891, 2022 | 18 | 2022 |
Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features PAM Zapata, O Méndez-Lucio, T Le, CJ Beese, J Wichard, D Rouquié, ... Digital discovery 2 (1), 91-102, 2023 | 11 | 2023 |
Representation learning on biomolecular structures using equivariant graph attention T Le, F Noe, DA Clevert Learning on Graphs Conference, 30: 1-30: 17, 2022 | 11 | 2022 |
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation T Le, J Cremer, F Noé, DA Clevert, K Schütt 12th International Conference on Learning Representations, 2023 | 6 | 2023 |
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling J Cremer, T Le, F Noé, DA Clevert, KT Schütt arXiv preprint arXiv:2405.14925, 2024 | | 2024 |
A community effort to discover small molecule SARS-CoV-2 inhibitors J Schimunek, P Seidl, K Elez, T Hempel, T Le, F Noé, S Olsson, L Raich, ... American Chemical Society (ACS), 2023 | | 2023 |
De novo drug design in continuous space T Le 10.5282/ubm/epub.70658, 2019 | | 2019 |
Supplementary Information for Neuraldecipher-Reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures T Le, R Winter, F Noé, DA Clevert | | |
Going full hyper: hyperbolic and hypercomplex graph embeddings for ADMET modeling T Le, M Bertolini, MA Boef, F Montanari, DA Clevert | | |