Drug discovery is a complex, expensive, and time-consuming process. It involves the identification of a target, screening of a large number of molecules, and optimization of the selected molecules. The process of drug discovery can be accelerated by using machine learning and deep learning.
Graduate Thesis
My graduate thesis focused on the application of deep learning to drug discovery, specifically building pharmacophore models for COVID-19 Mpro (Main Protease).
Approach
Binding Affinity Prediction: Using deep learning to predict the binding affinity of molecules to target proteins
Binding Site Identification: Finding the binding site of molecules on target proteins
Pharmacophore Modeling: Building pharmacophore models of molecules
Drug Screening: Screening large molecule databases for promising candidates
This work has been published in the following paper (Nguyen et al., 2022) and received the Best Paper Award at NICS 2022.
References
2022
Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism
Duc Q. Nguyen, Khoan D. Le, Bach T. Ly, and 7 more authors
In 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), Oct 2022
For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus.
@inproceedings{nguyen_towards_2022,title={Towards {De} {Novo} {Drug} {Design} for the {Coronavirus}: {A} {Drug}-{Target} {Interaction} {Prediction} {Approach} using {Atom}-enhanced {Graph} {Neural} {Network} with {Multi}-hop {Gating} {Mechanism}},copyright={Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License},shorttitle={Towards {De} {Novo} {Drug} {Design} for the {Coronavirus}},url={https://ieeexplore.ieee.org/document/10013437},doi={10.1109/NICS56915.2022.10013437},urldate={2025-09-08},booktitle={2022 9th {NAFOSTED} {Conference} on {Information} and {Computer} {Science} ({NICS})},author={Nguyen, Duc Q. and Le, Khoan D. and Ly, Bach T. and Nguyen, An D. and Nguyen, Quang H. and Nguyen, Tuan H. and Quan, Tho T. and Duong, Cuong Quoc and Nguyen, Phuong Thuy Viet and Truong, Thanh N.},month=oct,year={2022},keywords={Coronavirus, COVID-19, Drug-Target Interaction, Drugs, Graph neural networks, Graph Neural Networks, Multi-hop Gating Mechanism, Proteins, Spread spectrum communication, Transfer learning, Vaccines},pages={275--280},}