DIA-DB

DIA-DB predicts potential anti-diabetic small-molecule candidates by combining shape similarity comparison and inverse virtual screening against therapeutic protein targets to prioritize compounds for diabetes drug discovery.

Key Features:

• Shape Similarity Comparison: Employs shape similarity comparison against a curated database of approved antidiabetic drugs and experimental small molecules to identify structurally related candidates.
• Inverse Virtual Screening: Performs inverse virtual screening of user-input molecules against a set of therapeutic protein targets implicated in diabetes pathology to detect novel interactions and multi-target potential.
• Natural Product Screening: Applied screening of 867 compounds from over 300 African medicinal plants against 17 known anti-diabetic drug targets to evaluate plant-derived compounds.
• Integration with Experimental Profiling: Integrated into workflows involving extraction and LC-MS chemical profiling of Sclerocarya birrea to evaluate complex crude plant extracts.
• Identification of Multi-targeted Compounds and ADMET Consideration: Identifies compounds with favorable ADMET properties, including crotofoline A, erythraline, henningsiine, nauclefidine, vinburnine, and voaphylline.

Scientific Applications:

• Library Screening: Screening of large libraries of synthetic and natural compounds to prioritize anti-diabetic candidates.
• Natural Product Discovery: Identification of new bioactive compounds and plant sources with potential anti-diabetic activity from medicinal plants.
• ADMET and Drug-likeness Assessment: Early prediction of ADMET-related properties to support compound prioritization in drug development.
• Multi-target Therapeutic Exploration: Exploration and prioritization of multi-targeted small molecules for complex diseases such as diabetes.
• Integration with Analytical Chemistry: Coupling computational predictions with LC-MS chemical profiling to evaluate and prioritize constituents from crude extracts.

Methodology:

Performs shape similarity comparison against a curated database of approved antidiabetic drugs and experimental small molecules, conducts inverse virtual screening of compounds against a set of therapeutic protein targets, and has been applied to screen 867 compounds against 17 known anti-diabetic targets.