Attention is all you need: utilizing attention in AI-enabled drug discovery

Zhang, Yang; Liu, Caiqi; Liu, Mujiexin; Liu, Tianyuan; Lin, Hao; Huang, Cheng-Bing; Ning, Lin

doi:10.1093/bib/bbad467

Cited by 58 publications

(4 citation statements)

References 172 publications

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“…196 A critical aspect of virtual molecule screening is predicting how drug-like molecules (ligands) will interact with target proteins (receptors), considering factors like binding kinetics and atomic interactions. 197 EquiBind, a deep learning-based model, exemplifies innovation in this area. 198 It utilizes SE(3)-equivariant graph neural networks to predict bound protein–ligand conformations, incorporating both pose prediction and binding site identification.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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“…One of the key architectures used in PLMs is the transformer, which has revolutionized natural language processing and has been successfully adapted for protein sequence modeling. Transformers employ self-attention mechanisms that allow them to capture long-range dependencies between amino acid residues, enabling them to model the complex interactions that govern protein folding and function (Zhang et al, 2023).…”

Section: Protein Language Models: Harnessing Evolutionary Information...mentioning

confidence: 99%

From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

Kauffman

2024

Preprint

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Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.

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“…Zhang et al . [ 1 ] offer an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. The authors further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation.…”

Section: Reviewing the Progress Of Attention Mechanism Models In Prec...mentioning

confidence: 99%

Attention mechanism models for precision medicine

Cheng

2024

Briefings in Bioinformatics

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The development of deep learning models plays a crucial role in advancing precision medicine. These models enable personalized medical treatments and interventions based on the unique genetic, environmental and lifestyle factors of individual patients, and the promotion of precision medicine is achieved mainly through genomic data analysis, variant annotation and interpretation, pharmacogenomics research, biomarker discovery, disease typing, clinical decision support and disease mechanism interpretation. Extensive research has been conducted to address precision medicine challenges using attention mechanism models such as SAN, GAT and transformers. Especially, the recent popularity of ChatGPT has significantly propelled the application of this model type to a new height. Therefore, I propose a Special Issue for Briefings in Bioinformatics about the topic ‘Attention Mechanism Models for Precision Medicine’. This Special Issue aims to provide a comprehensive overview and presentation of innovative researches on the application of graph attention mechanism models in precision medicine.

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Attention is all you need: utilizing attention in AI-enabled drug discovery

Cited by 58 publications

References 172 publications

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

Attention mechanism models for precision medicine

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