Accelerate your research with cutting-edge computational services tailored for pharmaceutical, biotechnology, and academic sectors. From molecular dynamics to insilico drug design — we drive breakthroughs at the molecular level.
At Moleculytic, we specialize in advancing drug discovery through cutting-edge molecular dynamics simulations, focusing on the intricate behavior of proteins to accelerate scientific breakthroughs.
Our interdisciplinary team combines expertise in computational chemistry, biophysics, and machine learning to deliver insights that bridge the gap between in silico prediction and real-world therapeutic outcomes.
Trusted by leading pharmaceutical companies, biotech startups, and academic institutions worldwide, we bring precision and innovation to every research challenge — from hit identification to lead optimization.
All simulations validated against experimental data with peer-reviewed methodologies.
GPU-accelerated computing infrastructure enabling microsecond-scale simulations.
Ph.D. scientists and computational chemists with decades of combined experience.
150+ successful research projects across pharma, biotech, and academia.
From early-stage hit discovery to lead optimization, we provide the full computational spectrum.
Advanced atomistic and coarse-grained simulations of protein behavior, membrane systems, and biomolecular complexes. Providing nanosecond to microsecond timescale insights for drug-receptor interactions.
In-depth characterization of protein folding, conformational dynamics, binding site identification, and allosteric networks. Leveraging AlphaFold integration for structure prediction and validation.
Structure-based and ligand-based drug design workflows including pharmacophore modeling, scaffold hopping, and de novo design using generative AI models tailored for your target class.
High-throughput docking campaigns against large compound libraries (millions of compounds) to rapidly identify promising hit molecules against validated drug targets with precision ranking.
Rigorous FEP and MM-GBSA/PBSA calculations to rank and prioritize compounds by predicted binding affinity. Reliable relative and absolute binding free energy estimates for lead optimization.
Computational prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity profiles using AI-powered models to filter compounds early and reduce costly experimental failures.
Fully tailored computational workflows designed around your unique research challenges — from custom force field development to multi-scale modelling pipelines and ML-enhanced approaches.
We partner with universities and research institutes for joint publications, grant-funded projects, and hands-on workshops in computational drug discovery. Contact us to discuss research collaboration opportunities.
A structured, transparent workflow from initial consultation to final deliverables.
We understand your research objectives, target system, timeline, and specific computational requirements.
Our scientists design a bespoke computational workflow, selecting optimal methods and software for your system.
Execution of simulations on our HPC infrastructure with real-time progress updates and quality checks.
Comprehensive reports with visualizations, raw data, and actionable insights. Full IP transfer to your team.
Investigating conformational changes in kinase families and GPCRs through enhanced sampling MD simulations, revealing cryptic allosteric binding pockets for novel drug targeting.
Learn MoreEnd-to-end computational pipeline for antimicrobial resistance targets — from virtual screening of 10M+ compounds to FEP-validated leads with experimental confirmation partnership.
DiscoverIntegrating graph neural networks with classical MD to predict binding affinity and ADMET properties simultaneously, reducing optimization cycles from months to days.
ExploreWe integrate state-of-the-art deep learning, generative models, and graph neural networks into every stage of the drug discovery pipeline — transforming months of iteration into days of in silico insight.
Diffusion-based and variational autoencoder models generate novel drug-like scaffolds conditioned on target binding pocket geometry. We employ DiffSBDD, TargetDiff, and fragment-growing networks to explore uncharted chemical space.
Graph Attention Networks encode molecular topology and 3D conformation to predict binding free energies, IC₅₀ values, and selectivity profiles — outperforming classical QSAR across diverse target classes.
Transformer-based chemical language models fine-tuned on target-specific activity data for retrosynthesis prediction, reaction outcome forecasting, and property-guided lead elaboration at scale.
Combining AlphaFold2/3 and ESMFold predictions with MD relaxation and cryptic pocket detection to unlock undruggable targets — particularly intrinsically disordered proteins and novel GPCR families.
Ensemble deep learning models trained on curated pharmacokinetic datasets predict hERG cardiotoxicity, CYP inhibition, BBB permeability, and metabolic stability — integrated into virtual screening pipelines for early filtering.
RL agents trained with multi-objective reward signals (potency, selectivity, synthetic accessibility, ADMET) iteratively refine lead scaffolds — navigating vast chemical space with goal-directed molecular evolution.
Our integrated platform chains AlphaFold structure prediction → GNN virtual screening → generative lead exploration → RL-driven optimisation → deep ADMET filtering — all in one automated workflow delivering IND-ready candidates faster than ever.
Whether you're a pharmaceutical company seeking computational support, a biotech startup exploring new targets, or an academic group needing simulation expertise — we're here to help.