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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 …
that can predict three-dimensional (3D) structures of proteins from amino acid sequences …
Synthetic biology: bottom-up assembly of molecular systems
The bottom-up assembly of biological and chemical components opens exciting
opportunities to engineer artificial vesicular systems for applications with previously unmet …
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
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 …
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …
AlphaFold, artificial intelligence (AI), and allostery
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 …
biological sequence data and artificial intelligence (AI). AlphaFold has appended projects …
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …
innovatiaon and impact. However, advancement in this field requires formulation of …
Deep learning-based prediction of the T cell receptor–antigen binding specificity
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 …
small proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which …
Antibody structure prediction using interpretable deep learning
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 …
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
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 …
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
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
Fast end-to-end learning on protein surfaces
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 …
molecular surfaces. Recent works have shown that geometric deep learning can be used on …