Balancevo applies state-of-the-art computational chemistry, molecular modelling and machine learning to predict, explain and accelerate molecular interactions across pharmacy, chemistry, biology and sustainable technologies. Our in-silico workflows reduce experimental cost and time, de-risk early ideas, and deliver testable hypotheses that partners can validate in the lab.
What we do
Integration with experiment & workflows — deliver ranked candidate lists, mechanistic reports, and simulation-ready protocols for experimental validation.
Molecular docking & virtual screening — rapidly screen large libraries to prioritise candidate small molecules, peptides or ligands against a target (protein, RNA, material surface).
Molecular dynamics (MD) — simulate atomistic and coarse-grained dynamics to study conformational changes, binding stability, membrane and solvent effects, and transport phenomena over time.
Quantum chemistry & reaction modelling — compute reaction pathways, transition states, electronic structure and energetics (DFT, semi-empirical methods) for catalysis, degradation pathways and mechanistic insight.
Free energy calculations — estimate binding free energies (FEP, TI, MM/PB(GB)SA) to prioritise leads with higher confidence.
QM/MM and multiscale modelling — combine quantum and classical descriptions where needed (e.g., active sites in enzymes, materials interfaces).
Machine learning (ML) & data-driven models — train predictive models for physicochemical properties, ADMET endpoints, reaction yields, materials properties and biological activity using descriptors, graph networks and deep learning.
Generative modelling & de-novo design — use generative models to propose novel chemotypes with target property profiles and synthetically feasible scaffolds.
Typical applications
- Drug discovery & pharma: hit identification, lead optimization, binding mechanism analysis, predicting metabolic stability and toxicity, formulation interactions.
- Biotechnology & bioactive peptides: enzyme–substrate modelling, peptide design, protein engineering and docking to cell-surface receptors.
- Materials & energy: catalyst design, surface adsorption, electrolyte–electrode interactions, hydrogen interaction with materials and corrosion modelling.
- Environmental chemistry: degradation pathways, sorption to soils, and fate modelling of small molecules and nanomaterials.
- Agritech: small molecule or peptide biostimulants, biofertiliser–soil interactions, and microbe–plant molecular interfaces.
Methods & tools (examples)
We combine open and proprietary tools depending on project needs: molecular dynamics engines (GROMACS, NAMD, AMBER), docking tools (AutoDock Vina, Glide), quantum packages (ORCA, Gaussian), cheminformatics (RDKit), ML frameworks (PyTorch, TensorFlow, DeepChem), and workflow orchestration for reproducibility. We also deploy surrogate models and reduced representations when throughput is critical.
Why it matters
- Faster R&D: predict likely successes early and focus lab resources on the most promising candidates.
- Lower cost & risk: reduce expensive bench cycles through in-silico triage and robust computational validation.
- Mechanistic insight: simulations reveal how and why molecules interact — knowledge that helps rational design and regulatory explanation.
- Scalability: computational pipelines can screen thousands to millions of structures or run many scenario simulations for techno-economic or LCA coupling.
How Balancevo delivers
- Tailored pipelines and reproducible notebooks for partners.
- Clear decision-ready deliverables: ranked candidate lists, binding maps, free-energy estimates, reaction profiles, and ML models with explainability.
- Close coordination with experimental partners for iterative validate→refine cycles.
- Ethical, transparent modelling practices with documented assumptions and uncertainty quantification.