AIMC Topic: Drug Discovery

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Integrating convolutional layers and biformer network with forward-forward and backpropagation training.

Scientific reports Feb 28, 2025
Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compounds and accelerating therapeutic development. Traditional machine learning falters with high-dimensio...
Drug Discovery Humans Deep Learning Neural Networks, Computer Algorithms
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New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning.

PLoS biology Feb 28, 2025
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and stat...
Bacteria Ecology Biological Products Humans Machine Learning Anti-Bacterial Agents Artificial Intelligence Drug Discovery
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GLMCyp: A Deep Learning-Based Method for CYP450-Mediated Reaction Site Prediction.

Journal of chemical information and modeling Feb 27, 2025
Cytochrome P450 enzymes (CYP450s) play crucial roles in metabolizing many drugs, and thus, local chemical structure can profoundly influence drug efficacy and toxicity. Therefore, the accurate prediction of CYP450-mediated reaction sites can increase...
Humans Cytochrome P-450 Enzyme System Drug Discovery Deep Learning
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A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network.

Methods (San Diego, Calif.) Feb 26, 2025
It is a vital step to identify the enzyme turnover number (kcat) in synthetic biology and early-stage drug discovery. Recently, deep learning methods have achieved inspiring process to predict kcat with the development of multi-species enzyme-substra...
Kinetics Synthetic Biology Neural Networks, Computer Algorithms Drug Discovery Substrate Specificity Deep Learning Enzymes
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Large Model Era: Deep Learning in Osteoporosis Drug Discovery.

Journal of chemical information and modeling Feb 26, 2025
Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in a decline in patients' life quality. In response to the increased incidence of osteoporosis, related dr...
Osteoporosis Drug Discovery Humans Deep Learning
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Recent advances in AI-driven protein-ligand interaction predictions.

Current opinion in structural biology Feb 24, 2025
Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligan...
Drug Discovery Ligands Protein Binding Deep Learning Artificial Intelligence Humans Binding Sites Molecular Docking Simulation Proteins
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Natural Language Processing Methods for the Study of Protein-Ligand Interactions.

Journal of chemical information and modeling Feb 24, 2025
Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and developmen...
Ligands Proteins Natural Language Processing Humans Machine Learning Drug Discovery Protein Binding
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Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.

Journal of chemical information and modeling Feb 24, 2025
Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for ...
Artificial Intelligence Deep Learning Reward Machine Learning Drug Discovery
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A machine learning method for predicting molecular antimicrobial activity.

Scientific reports Feb 24, 2025
In response to the increasing concern over antibiotic resistance and the limitations of traditional methods in antibiotic discovery, we introduce a machine learning-based method named MFAGCN. This method predicts the antimicrobial efficacy of molecul...
Anti-Infective Agents Drug Discovery Anti-Bacterial Agents Machine Learning
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Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

Journal of chemical information and modeling Feb 23, 2025
While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine le...
Microbial Sensitivity Tests Bacteria Small Molecule Libraries Anti-Infective Agents Machine Learning Drug Discovery Anti-Bacterial Agents
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