AQBioSim

Alchemite provides AI-augmented physical modeling and solutions that help organizations extract actionable insights from experimental and simulation data by combining machine learning with physics-informed models to improve prediction accuracy, reduce experimental costs, and optimize product and process development. Its solutions span materials discovery and design, predictive modelling of performance and reliability, multiscale modelling that connects atomistic to macroscopic behaviour, and automation of workflow tasks such as data integration, surrogate modelling, and model validation. It supports physics-aware neural networks and hybrid modelling approaches that respect underlying scientific laws while learning from data to enable faster and more accurate simulations, reduced reliance on expensive physical testing, and improved decision-making. Intellegens’ tools are applied in areas such as battery performance prediction, chemical process optimization, etc.

alvaBuilder is a no-code de novo molecular design software for generating novel chemical structures that satisfy user-defined structural, physicochemical, and modeling constraints. It enables the creation of new molecules starting from scratch or by evolving existing structures using fragment-based and rule-driven approaches. alvaBuilder integrates seamlessly with QSAR/QSPR workflows, allowing users to guide molecule generation using predictive models, descriptor ranges, and property targets. The software supports medicinal chemistry, lead optimization, and virtual screening tasks by efficiently exploring chemical space while maintaining chemical feasibility and interpretability. alvaBuilder is designed for research and industrial applications where transparent, controllable, and reproducible molecular generation is required.