Our Drug Discovery Pipeline
A complete computational toolkit for every stage of the drug discovery journey
Target Identification
Identify and validate biological targets using computational methods, protein structure analysis, and literature-driven target prioritization.
Virtual Screening & Hit Identification
Screen millions of compounds in silico using structure-based (molecular docking with AutoDock Vina, RxDock) and ligand-based approaches to find promising hits.
ADMET & Toxicity Prediction
Predict absorption, distribution, metabolism, excretion, and toxicity properties early using validated AI/ML models — before costly synthesis.
De Novo Molecular Design
Generate novel, drug-like molecules using our hybrid reinforcement learning framework combining GPT-based generative models with multi-objective optimization and retrosynthetic feasibility analysis.
Lead Optimization
Multi-parameter optimization guided by machine learning to improve potency, selectivity, and drug-likeness of lead compounds.
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Purpose-built platforms for computational drug discovery
Enalos Asclepios KNIME Nodes
A user-friendly toolkit offering state-of-the-art functionalities for preparing bioinformatics and cheminformatics components in molecular docking and molecular dynamics simulations.
- Zero-code philosophy
- Seamless KNIME integration
- Molecular docking (AutoDock Vina & RxDock)
- Structure-based drug design workflows
- Reproducible computational pipelines
Isalos Analytics Platform
End-to-end workflow automation for data manipulation, machine learning model development, and predictive analytics — no programming required.
- AutoML & Advanced Statistics
- No-code environment
- Comprehensive Design of Experiments suite
- QSAR/QSPR model building
- Data-driven decision making
Generative AI for De Novo Drug Design
Designing novel molecules that don't yet exist in any chemical library
NovaMechanics has developed a hybrid reinforcement learning framework that combines a GPT-based generative model with multi-objective optimization for de novo drug design. Unlike traditional virtual screening that searches existing libraries, our generative approach explores uncharted chemical space to create entirely novel, synthesizable molecules.
The framework integrates machine learning-based binding affinity predictions with periodic docking scores, guiding the RL agent towards chemically diverse, target-specific molecules. Retrosynthetic pathway predictions confirm the synthetic accessibility of generated compounds, accelerated by our Enalos Asclepios KNIME nodes.
See It in Action
Real-world projects where our pipeline delivered validated results
RNSMOKE: Natural CYP2A6 Inhibitors for Smoking Cessation
Virtual screening of 700,000+ natural products, ML modeling with Isalos, and in vivo validation identified novel compounds with IC50 of 0.64 µM — comparable to the gold standard.
Read Case StudyChemBioAD: Multi-Target BACE Inhibitors for Alzheimer's Disease
Structure-based screening, consensus ML models, and multi-target validation discovered a potent BACE1 inhibitor (IC50 = 160 nM) with dual activity against tau aggregation.
Read Case StudyChemBioCOMT: Novel COMT Inhibitors for Parkinson's Disease
Ligand-based ML, structure-based docking of 14,400 molecules, and in vitro/in vivo testing discovered a novel COMT inhibitor (IC50 = 8.1 μM mCOMT) with zero cytotoxicity.
Read Case StudySmall-Molecule PPI Dual Inhibitors of TNF & RANKL
Cheminformatics pipeline screened ~15,000 molecules to discover T8 & T23 — dual TNF/RANKL inhibitors with low toxicity. Featured in EurekAlert & MS News Today.
Read Case StudyPlant-Origin Natural Product Inhibitors of TNF & RANKL
In silico screening of plant-origin NPs discovered the first natural product TNF inhibitors, including A11 (Ampelopsin H) — the first NP dual TNF/RANKL inhibitor.
Read Case StudyEnalos Asclepios Pipeline for TNF Inhibitor Discovery
Advanced Enalos Asclepios KNIME nodes with 1000ns MD simulations identified Nepalensinol B, which completely abolished TNF-TNFR1 binding.
Read Case StudyReady to Accelerate Your Drug Discovery?
Let's discuss how our computational pipeline can help you find better candidates faster.
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