Tuan Le

📍 Berlin, Germany

Hi! My name is Tuan and I am a 4th year Ph.D. student working in the Pfizer Machine Learning Research group in collaboration with the AI4Sciences group from FU Berlin, supervised by Djork-Arné Clevert and Frank Noé.
My research interest focuses on representation learning on molecular structures, including drug compounds and proteins using methodologies from deep learning, such as recurrent- or graph neural networks in combination with generative learning algorithms to sample novel molecules.

selected publications

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Tuan Le, Julian Cremer, Frank Noe, Djork-Arné Clevert, and 1 more author

In The Twelfth International Conference on Learning Representations 2024

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Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptimal performance on large molecular structures and limited training data. To address this gap, we explore the design space of E(3)-equivariant diffusion models, focusing on previously unexplored areas. Our extensive comparative analysis evaluates the interplay between continuous and discrete state spaces. From this investigation, we present the EQGAT-diff model, which consistently outperforms established models for the QM9 and GEOM-Drugs datasets. Significantly, EQGAT-diff takes continuous atom positions, while chemical elements and bond types are categorical and uses time-dependent loss weighting, substantially increasing training convergence, the quality of generated samples, and inference time. We also showcase that including chemically motivated additional features like hybridization states in the diffusion process enhances the validity of generated molecules. To further strengthen the applicability of diffusion models to limited training data, we investigate the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogen atoms to target different data distributions. Fine-tuning EQGAT-diff for just a few iterations shows an efficient distribution shift, further improving performance throughout data sets. Finally, we test our model on the Crossdocked data set for structure-based de novo ligand generation, underlining the importance of our findings showing state-of-the-art performance on Vina docking scores.
Representation Learning on Biomolecular Structures using Equivariant Graph Attention

Tuan Le, Frank Noé, and Djork-Arné Clevert

In Learning on Graphs Conference 2022

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Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in the development of biotherapeutics. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage the geometric and relational detail of the problem domain and are known to learn expressive representations through the propagation of information between nodes leveraging higher-order representations to faithfully express the geometry of the data, such as directionality in their intermediate layers. In this work, we propose an equivariant GNN that operates with Cartesian coordinates to incorporate directionality and we implement a novel attention mechanism, acting as a content and spatial dependent filter when propagating information between nodes. Our proposed message function processes vector features in a geometrically meaningful way by mixing existing vectors and creating new ones based on cross products. We demonstrate the efficacy of our architecture on accurately predicting properties of large biomolecules and show its computational advantage over recent methods which rely on irreducible representations by means of the spherical harmonics expansion.
Parameterized Hypercomplex Graph Neural Networks for Graph Classification

Tuan Le, Marco Bertolini, Frank Noé, and Djork-Arné Clevert

In Artificial Neural Networks and Machine Learning – ICANN 2021 2021

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Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quaternion algebras, which have been found in several contexts to enable effective representation learning that inherently incorporates a weight-sharing mechanism, we develop graph neural networks that leverage the properties of hypercomplex feature transformation. In particular, in our proposed class of models, the multiplication rule specifying the algebra itself is inferred from the data during training. Given a fixed model architecture, we present empirical evidence that our proposed model incorporates a regularization effect, alleviating the risk of overfitting. We also show that for fixed model capacity, our proposed method outperforms its corresponding real-formulated GNN, providing additional confirmation for the enhanced expressivity of HC embeddings. Finally, we test our proposed hypercomplex GNN on several open graph benchmark datasets and show that our models reach state-of-the-art performance while consuming a much lower memory footprint with 70% fewer parameters. Our implementations are available at https://github.com/bayer-science-for-a-better-life/phc-gnn.
Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures

Tuan Le, Robin Winter, Frank Noé, and Djork-Arné Clevert

Chem. Sci. 2020

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Protecting molecular structures from disclosure against external parties is of great relevance for industrial and private associations, such as pharmaceutical companies. Within the framework of external collaborations, it is common to exchange datasets by encoding the molecular structures into descriptors. Molecular fingerprints such as the extended-connectivity fingerprints (ECFPs) are frequently used for such an exchange, because they typically perform well on quantitative structure–activity relationship tasks. ECFPs are often considered to be non-invertible due to the way they are computed. In this paper, we present a fast reverse-engineering method to deduce the molecular structure given revealed ECFPs. Our method includes the Neuraldecipher, a neural network model that predicts a compact vector representation of compounds, given ECFPs. We then utilize another pre-trained model to retrieve the molecular structure as SMILES representation. We demonstrate that our method is able to reconstruct molecular structures to some extent, and improves, when ECFPs with larger fingerprint sizes are revealed. For example, given ECFP count vectors of length 4096, we are able to correctly deduce up to 69% of molecular structures on a validation set (112 K unique samples) with our method.