Mentorship

My mentoring style is strongly project-centered. I aim to connect students and early-career collaborators with real research problems, reproducible workflows, and tangible scientific outputs, including software, benchmarks, manuscripts, and conference papers.

Master students

2024–2025

Phan Tuyet Minh

Project: ProQSAR

Focus: reproducible QSAR modeling, evaluation design, and predictive workflows for small-data settings.

Output: ProQSAR: A Modular and Reproducible Framework for Small-Data QSAR Modeling with Fit-and-Use Models

Current position: PhD student, TACsy.

Bachelor students

2022–2023

The-Chuong Trinh

Focus: machine learning, feed-forward neural networks, 3D image-based convolutional neural networks, and graph neural networks for ALK-targeted modeling.

Outputs:

• Synergy of advanced machine learning and deep neural networks with consensus molecular docking for virtual screening of anaplastic lymphoma kinase inhibitors

• A Graph Neural Network Model Enables Accurate Prediction of Anaplastic Lymphoma Kinase Inhibitors Compared to Other Machine Learning Models

Current position: PhD student, Université Grenoble Alpes, France.

Hoang-Son Lai Le

Focus: comparative evaluation of molecular docking tools and graph neural networks for predicting ligand interactions with the tubulin receptor.

Current position: PhD student, Texas A&M University, USA.

Gia-Bao Truong

Focus: molecular optimization targeting VEGFR through variational autoencoders and generative modeling.

Output: Discovery of Vascular Endothelial Growth Factor Receptor 2 Inhibitors Employing Junction Tree Variational Autoencoder with Bayesian Optimization and Gradient Ascent

Current position: Erasmus Mundus master’s student in cheminformatics.

Xuan-Truc Dinh Tran

Focus: similarity searching, classical pharmacophore modeling, and ensemble learning.

Output: Integration of the Butina Algorithm and Ensemble Learning Strategies for the Advancement of a Pharmacophore Ligand-Based Model: An In Silico Investigation of Apelin Agonists

2023–2024

Van-Thinh To

Focus: molecular representation learning through integration chemical knowledge into graph neural networks.

Outputs:

• Innovative Virtual Screening of PD-L1 Inhibitors: The Synergy of Molecular Similarity, Neural Networks and GNINA Docking

• KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions

Current position: Erasmus Mundus master’s student in cheminformatics.

Thanh-An Pham

Focus: molecular docking workflows combined with graph neural networks to predict ligand conformations in protein binding.

Current position: Erasmus Mundus master’s student in cheminformatics.

Phuoc-Chung Van Nguyen

Focus: graph neural networks for reaction classification.

Output: SynCat: Molecule-Level Attention Graph Neural Network for Precise Reaction Classification

Current position: Erasmus Mundus master’s student in cheminformatics.

2024–2025

Nhu-Ngoc Nguyen Song

Focus: benchmark development, decoy design, and evaluation of reaction prediction models.

Output: SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling

Current position: Erasmus Mundus master’s student in cheminformatics.

What I usually support

• project framing and literature orientation

• workflow design and reproducible coding practice

• evaluation design and interpretation of results

• documentation, writing, and figure preparation

• translation of exploratory work into reusable scientific outputs