Way2Drug platform is developed and supported by the multidisciplinary team of researchers working in bioinformatics, chemoinformatics and computer-aided drug discovery for over thirty years.
In an era when drug development costs reach billions of dollars
The core concept of the Way2Drug platform is the application of structure-property associations, structure-activity relationships, and machine learning for systematic prediction of biological activity spectra of chemical compounds based solely on their structural formulas. The approach assumes that extensive experimental data on hundreds of thousands of compounds can be translated into rigorous machine learning (Q)SAR/SSPR models, enabling prediction of the properties of novel molecules prior to synthesis and biological testing.
In this way, the platform acts as a bridge between chemoinformatics and experimental pharmacology, converting molecular structure into actionable predictions of pharmacological effects, mechanisms of action, toxicity, metabolism, and therapeutic potential. This paradigm reflects the ongoing transformation of drug discovery from empirical screening to rational, data-driven design.
The platform encompasses a number of specialized assets that address different stages of drug discovery and development, including:
databases of approved drugs and phytochemicals
prediction of biological activities, metabolism and toxicity
text analytics, SEQ analysis, etc.
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Specialized tools for prediction of biological activity, toxicity, metabolism, and drug interactions from molecular structures
explores how bioactive compounds interact with living systems. Way2Drug translates these interactions into predictions of pharmacological effects, mechanisms of action, and side effects directly from molecular structure, helping researchers identify the most promising candidates faster.
Read moreproperties are what separate a promising molecule from a viable drug. Way2Drug tools assess absorption, distribution, metabolism, excretion, and toxicity in real time. They flag liabilities via drug-likeness filters, helping researchers identify failures early and prioritize leads with confidence.
Read moretransforms scientific literature into actionable pharmacological intelligence. Using text mining and machine learning, it extracts molecular relationships to build evidence-based disease-target–ligand networks, accelerating hypothesis generation, target validation, and clever drug discovery decisions.
Read moredefine predictive quality. Way2Drug knowledge bases combine expert-validated structures and annotated biological activity profiles under strict quality control, ensuring consistency and scientific integrity. This provides the full modeling lifecycle, from training to validation, keeping outputs evidence-based, reproducible, and decision-ready.
Read moreA manually curated collection of databases for training predictive models
Comprehensive database of approved small-molecule drugs from official medicine registers across 88 countries worldwide
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database of secondary metabolites from medicinal plants included in the State Pharmacopoeia of the Russian Federation
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database dedicated the biotransformation of drug-like compounds by the human gut microbiota
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database containing 1,651 amino acid sequences of HIV structural proteins, treatment history information, and CD4+ cell count and viral load data
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database was compiled and presented the key genes, biological pathways and master regulators associated with the development of major depressive disorder
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targets of approved drugs, as well as targets from clinical, preclinical, and promising studies for developing new treatments for socially significant diseases
Read moreWe invite you to familiarize yourself with the technologies, web services, databases, and software products developed our scientific team.
Way2Drug platform is developed and supported by the multidisciplinary team of researchers working in bioinformatics, chemoinformatics and computer-aided drug discovery for about thirty years. We have proposed the local correspondence concept, a novel bioinformatics and chemoinformatics paradigm, which is based on the fact that most biological activities of drug-like organic compounds are the result of molecular recognition and depend on the correspondence between the particular atoms of the ligand and the target.
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