AlphaFold2 and its applications in the fields of biology and medicine

Z Yang, X Zeng, Y Zhao, R Chen - Signal Transduction and Targeted …, 2023 - nature.com
Abstract AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind
that can predict three-dimensional (3D) structures of proteins from amino acid sequences …

Synthetic biology: bottom-up assembly of molecular systems

S Hirschi, TR Ward, WP Meier, DJ Müller… - Chemical …, 2022 - ACS Publications
The bottom-up assembly of biological and chemical components opens exciting
opportunities to engineer artificial vesicular systems for applications with previously unmet …

Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design

A Campbell, J Yim, R Barzilay, T Rainforth… - arxiv preprint arxiv …, 2024 - arxiv.org
Combining discrete and continuous data is an important capability for generative models.
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …

AlphaFold, artificial intelligence (AI), and allostery

R Nussinov, M Zhang, Y Liu, H Jang - The Journal of Physical …, 2022 - ACS Publications
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of
biological sequence data and artificial intelligence (AI). AlphaFold has appended projects …

Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development

K Huang, T Fu, W Gao, Y Zhao, Y Roohani… - arxiv preprint arxiv …, 2021 - arxiv.org
Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …

Deep learning-based prediction of the T cell receptor–antigen binding specificity

T Lu, Z Zhang, J Zhu, Y Wang, P Jiang, X **ao… - Nature machine …, 2021 - nature.com
Neoantigens play a key role in the recognition of tumour cells by T cells; however, only a
small proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which …

Antibody structure prediction using interpretable deep learning

JA Ruffolo, J Sulam, JJ Gray - Patterns, 2022 - cell.com
Therapeutic antibodies make up a rapidly growing segment of the biologics market.
However, rational design of antibodies is hindered by reliance on experimental methods for …

Drugood: Out-of-distribution dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations

Y Ji, L Zhang, J Wu, B Wu, L Li, LK Huang… - Proceedings of the …, 2023 - ojs.aaai.org
AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search
for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use …

[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review

F Soleymani, E Paquet, H Viktor, W Michalowski… - Computational and …, 2022 - Elsevier
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …

Fast end-to-end learning on protein surfaces

F Sverrisson, J Feydy, BE Correia… - Proceedings of the …, 2021 - openaccess.thecvf.com
Proteins' biological functions are defined by the geometric and chemical structure of their 3D
molecular surfaces. Recent works have shown that geometric deep learning can be used on …