AIMC Topic: Structure-Activity Relationship

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Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on the Chemical Structure.

International journal of molecular sciences May 19, 2025
Antibody-drug conjugates (ADCs) are promising cancer therapeutics, but optimizing their cytotoxic payloads remains challenging. We present DumplingGNN, a novel hybrid Graph Neural Network architecture for predicting ADC payload activity and toxicity....
Humans Antineoplastic Agents Structure-Activity Relationship Immunoconjugates Neural Networks, Computer Neoplasms Drug Discovery
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Data-Driven Insights into Porphyrin Geometry: Interpretable AI for Non-Planarity and Aromaticity Analyses.

Journal of chemical information and modeling May 12, 2025
Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for...
Porphyrins Ligands Machine Learning Structure-Activity Relationship Models, Molecular
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Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.

Journal of biomolecular structure & dynamics May 1, 2025
Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a c...
T-Lymphocytes Molecular Docking Simulation Drug Discovery Humans Machine Learning Thermodynamics Structure-Activity Relationship Molecular Dynamics Simulation Protein Binding Protein Kinase Inhibitors Signal Transduction Protein Serine-Threonine Kinases
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Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2025
The elucidation of protein structure and function plays a pivotal role in understanding biological processes and facilitating drug discovery. With the exponential growth of protein sequence data, machine learning techniques have emerged as powerful t...
Natural Language Processing Proteins Humans Protein Conformation Databases, Protein Neural Networks, Computer Machine Learning Structure-Activity Relationship Deep Learning Computational Biology
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Fighting COVID-19 with Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is ...
Structure-Activity Relationship Molecular Structure COVID-19 Drug Treatment Drug Discovery Antiviral Agents Drug Repositioning Host-Pathogen Interactions Humans Artificial Intelligence Machine Learning COVID-19 SARS-CoV-2 Animals
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Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

Briefings in bioinformatics Nov 5, 2021
With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-qual...
Algorithms Quantum Theory Drug Design Cheminformatics Humans Drug Discovery Deep Learning Structure-Activity Relationship Models, Molecular
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De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Briefings in bioinformatics Nov 5, 2021
Artificial intelligence, such as deep generative methods, represents a promising solution to de novo design of molecules with the desired properties. However, generating new molecules with biological activities toward two specific targets remains an ...
Structure-Activity Relationship Ligands Chemical Phenomena Databases, Pharmaceutical Artificial Intelligence Algorithms Drug Discovery Biomarkers Deep Learning Drug Design Proteins
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The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.

Briefings in bioinformatics Nov 5, 2021
Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. ...
Protein Interaction Domains and Motifs Machine Learning Structure-Activity Relationship Algorithms Protein Binding Neoplasms Disease Susceptibility Workflow Computational Biology Peptides Amino Acid Sequence Gene Ontology Databases, Genetic Phylogeny Lectins
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SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

Briefings in bioinformatics Nov 5, 2021
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computat...
Computational Biology Structure-Activity Relationship Neural Networks, Computer Drug Interactions Software
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AlphaFold2 and the future of structural biology.

Nature structural & molecular biology Sep 1, 2021
Protein Folding Deep Learning Algorithms Sequence Alignment Computational Biology Internet Structure-Activity Relationship Amino Acid Sequence Protein Conformation
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