AI Medical Compendium Topic

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Structure-Activity Relationship

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Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

Briefings in bioinformatics
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...

De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Briefings in bioinformatics
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 ...

The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.

Briefings in bioinformatics
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. ...

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

Briefings in bioinformatics
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...

Machine learning approaches for elucidating the biological effects of natural products.

Natural product reports
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural pr...

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM, pharmacophore and docking model.

Journal of enzyme inhibition and medicinal chemistry
Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLI...

Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems.

NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...

Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review.

Current computer-aided drug design
Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on th...