Studies
Computational Chemistry
Quantum mechanics (DFT), molecular dynamics, QSAR, and cheminformatics to characterize and predict molecules or systems.
Quantum chemistry
First-principles predictions of structure, energetics, spectra, and reaction mechanisms.
Molecular dynamics
Time-dependent insights into conformations, bindings, stability, and thermodynamics and kinetics.
QSAR machine learning
Translations of structure-property data into predictive maps of potency and developability.
Cheminformatics
Informatics at scale through representation, search, and reaction-aware analytics for triage and libraries.
Bioinformatics & Biochemistry
GOI and protein modelling, mutational hypotheses, and structure-guided insights to accelerate laboratory programs.
Protein structure prediction
Comparative modeling to produce a 3D structure for experimental planning and biologic design.
Biomolecular molecular dynamics
An ensemble view of conformations, flexibilities, functions, and motions for decision-useful predictions.
Mutational analysis and protein engineering
Substitutions and sequence variants for improved stability, activity, specificity, and expression.
Mechanistic and interaction modelling
Electrostatics, free-energy, and QM/MM to quantify the energetic determinants of function and reactivity.
Applied Physics & Engineering
Modelling, approximations, and simulation analyses that prompt the definition and derive the predictability of a physical system.
Continuum and field-based simulation
CFD and electromagnetic or elastic PDE solvers to predict spatiotemporal fields from first principles for performance, safety margins, and design.
Multiscale and reduced-order modeling
Tractable surrogates that preserve dominant mechanisms, enabling rapid parameter sweeps, uncertainty quantification, and real-time decision support.
Statistical physics and stochastic simulation
Equilibrium distribution methods for phase behavior, fluctuation-driven effects, and macroscopic observables inferred from microscopic rules.
Inverse problems and data assimilation
Integrated models to infer latent parameters, calibrate predictive systems, and generate defensible forecasts with explicitly quantified uncertainty.