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Drug Design

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Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design.

Expert opinion on drug discovery
INTRODUCTION: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs ofte...

Susana Vázquez Torres: the power of computational protein design.

Bulletin of the World Health Organization
Susana Vázquez Torres talks to Ana Lesher Treviño about AI-guided protein design for antivenoms and her aim to improve access to lifesaving treatments in low-resource settings.

Self-awareness of retrosynthesis via chemically inspired contrastive learning for reinforced molecule generation.

Briefings in bioinformatics
The recent progress of deep generative models in modeling complex real-world data distributions has enabled the generation of novel compounds with potential therapeutic applications for various diseases. However, most studies fail to optimize the pro...

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.

Journal of chemical information and modeling
This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMI...

Structure-based artificial intelligence-aided design of MYC-targeting degradation drugs for cancer therapy.

Biochemical and biophysical research communications
The MYC protein is an oncoprotein that plays a crucial role in various cancers. Although its significance has been well recognized in research, the development of drugs targeting MYC remains relatively slow. In this study, we developed a novel MYC pe...

DrugGen enhances drug discovery with large language models and reinforcement learning.

Scientific reports
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...

Bioactive structures for inhibitors of polymerase enzyme by artificial intelligence.

Future medicinal chemistry
AIMS: Present new bioactive compounds, created by De novo Drug Design and artificial intelligence (AI), as possible inhibitors of polymerase.

Knowledge-aware contrastive heterogeneous molecular graph learning.

PLoS computational biology
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowled...

Sculpting molecules in text-3D space: a flexible substructure aware framework for text-oriented molecular optimization.

BMC bioinformatics
The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research. However, the challenge of designi...

AI-designed antibody candidates hit a crucial target.

Science (New York, N.Y.)
Companies find enticing drug leads that bind to tricky cell membrane proteins.