The platform integrates computational chemistry, machine learning and structural biology to evaluate multiple development criteria simultaneously, with a focus on toxicity reduction, selectivity, biological relevance and clinical viability. Its workflow incorporates molecular docking, molecular dynamics and free-energy calculations, alongside advanced machine-learning models, including graph neural networks, to predict off-target interactions and other factors relevant to candidate development.
Platform
AI & Data Science
Alosenta has developed a proprietary AI and machine-learning platform designed to support end-to-end drug discovery, spanning de novo molecule generation, hit-to-lead optimisation, toxicity and off-target prediction, high-throughput virtual screening and drug combination optimisation.
From Scientific Potential to Development Decisions
The platform is trained on curated clinical and experimental datasets and is designed to support the early identification and prioritisation of viable candidates before significant resources are committed to downstream development.
In addition to scientific and biological analysis, Alosenta incorporates cost-benefit modelling and financial risk analysis, helping prioritise candidates based on both their potential clinical value and commercial viability.
Explainable and Integrated by Design
Unlike conventional black-box approaches, Alosenta's platform is designed around explainability, reproducibility and data provenance.
Each stage of the workflow contributes to a broader multi-objective optimisation process, allowing scientists to understand not only which candidates or combinations are prioritised, but also the underlying molecular and biological rationale.
The platform provides a scalable approach to therapeutic discovery and is designed to work alongside Alosenta’s targeted delivery technologies, connecting AI-driven drug and combination discovery with the development of targeted therapeutic systems.
By combining AI, computational chemistry, structural biology and experimental evidence, Alosenta aims to identify promising drug candidates and combinations earlier, reduce downstream development risk and support better scientific and commercial decision-making.
