Biology subjects

Nguyen, V. T. D.

Publications and source records attributed to Nguyen, V. T. D..

4 recordsLinked to original sources

bioRxiv2024

Generative AI-assisted Virtual Screening Pipeline for Generalizable and Efficient Drug Repurposing

Drug repurposing presents a valuable strategy to expedite drug discovery by identifying new therapeutic uses for existing compounds, especially for diseases with limited treatment options. We propose a Generative AI-assisted Virtual Screening Pipeline that combines generative modeling, binding pocket prediction, and similarity-based searches within drug databases to achieve a generalizable and efficient approach to drug repurposing. Our pipeline enables blind screening of any protein target without requiring prior structural or functional knowledge, allowing it to adapt to a wide range of diseases, including emerging health threats and novel targets where information is scarce. By rapidly generating potential ligands and efficiently identifying and ranking drug candidates, our approach accelerates the drug discovery process, broadening the scope and impact of repurposing efforts and offering new possibilities for therapeutic development. Detailed results and implementation can be accessed at https://github.com/HySonLab/DrugPipe.

bioinformatics↗

bioRxiv2024

Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models

Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an efficient computational framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiffs potential to advance computational drug discovery and protein engineering. Our source code is publicly available at https://github.com/HySonLab/Protein_Redesign.

bioinformatics↗

bioRxiv2024

Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design

Directed evolution has been the most effective method for protein engineering that optimizes biological functionalities through a resource-intensive process of screening or selecting among a vast range of mutations. To mitigate this extensive procedure, recent advancements in machine learning-guided methodologies center around the establishment of a surrogate sequence-function model. In this paper, we propose Latent-based Directed Evolution (LDE), an evolutionary algorithm designed to prioritize the exploration of high-fitness mutants in the latent space. At its core, LDE is a regularized variational autoencoder (VAE), harnessing the capabilities of the state-of-the-art Protein Language Model (pLM), ESM-2, to construct a meaningful latent space of sequences. From this encoded representation, we present a novel approach for efficient traversal on the fitness landscape, employing a combination of gradient-based methods and directed evolution. Experimental evaluations conducted on eight protein sequence design tasks demonstrate the superior performance of our proposed LDE over previous baseline algorithms. Our implementation is publicly available at https://github.com/HySonLab/LatentDE.

bioinformatics↗

bioRxiv2023

Multimodal Pretraining for Unsupervised Protein Representation Learning

In this paper, we introduce a framework of symmetry-preserving multimodal pretraining to learn a unified representation of proteins in an unsupervised manner, encompassing both primary and tertiary structures. Our approach involves proposing specific pretraining methods for sequences, graphs, and 3D point clouds associated with each protein structure, leveraging the power of large language models and generative models. We present a novel way to combining representations from multiple sources of information into a single global representation for proteins. We carefully analyze the performance of our framework in the pretraining tasks. For the fine-tuning tasks, our experiments have shown that our new multimodal representation can achieve competitive results in protein-ligand binding affinity prediction, protein fold classification, enzyme identification and mutation stability prediction. We expect that this work will accelerate future research in proteins. Our source code in PyTorch deep learning framework is publicly available at https://github.com/HySonLab/Protein_Pretrain.

bioinformatics↗