AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 151 to 160 of 934 articles

Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad applicati...

Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery.

Journal of chemical information and modeling
Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, rem...

Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow.

Journal of chemical information and modeling
Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure-property relationships within materials databases. This approach holds significant importance in drug discovery and...

Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.

Journal of chemical information and modeling
Peptides are crucial in biological processes and therapeutic applications. Given their importance, advancing our ability to predict peptide properties is essential. In this study, we introduce Multi-Peptide, an innovative approach that combines trans...

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

Journal of chemical information and modeling
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and sele...

AI-Driven Drug Discovery for Rare Diseases.

Journal of chemical information and modeling
Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, m...

Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling
Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges d...

ProtChat: An AI Multi-Agent for Automated Protein Analysis Leveraging GPT-4 and Protein Language Model.

Journal of chemical information and modeling
Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein...

ReLMM: Reinforcement Learning Optimizes Feature Selection in Modeling Materials.

Journal of chemical information and modeling
A challenge to materials discovery is the identification of the physical features that are most correlated to a given target material property without redundancy. Such variables necessarily comprise the optimal search domain in subsequent material de...

Hither-CMI: Prediction of circRNA-miRNA Interactions Based on a Hybrid Multimodal Network and Higher-Order Neighborhood Information via a Graph Convolutional Network.

Journal of chemical information and modeling
Numerous studies show that circular RNA (circRNA) functions as a sponge for microRNA (miRNA), significantly regulating gene expression by interacting with miRNA, which in turn affects the progression of human diseases. Traditional experimental approa...