# Pharmacophore.in — AI-Powered Drug Discovery & Computational Chemistry Suite

> Pharmacophore is an end-to-end, web-based computational drug discovery and biopharmaceutical AI platform. It integrates target discovery, de novo generative chemistry, molecular docking, GPU-accelerated molecular dynamics (MD), lead optimization, formulation stability, lipid solubility prediction, and statistical design of experiments (DoE).

## Core AI & Computational Chemistry Engines

- [Target Selection & PPI Omics (NetForge)](https://pharmacophore.in/tools/netforge): Multi-omics target discovery, protein-protein interaction (PPI) network topology (degree, betweenness, hub detection), and Gene Ontology (GO) / Reactome pathway enrichment.
- [De Novo Molecular Generation (PharmForge AI)](https://pharmacophore.in/tools/pharmforge-ai): Deep generative chemistry utilizing reinforcement learning, SMILES validation, Lipinski/Veber drug-likeness filtering, and automated ADMET profiling.
- [Molecular Docking & Affinity Scoring (DockForge)](https://pharmacophore.in/tools/dockforge): High-throughput protein-ligand docking powered by AutoDock Vina, automated pocket/grid box detection, binding energy calculation (kcal/mol), and interactive 3D NGL/Mol* complex visualization.
- [GPU Molecular Dynamics Simulation (MDForge)](https://pharmacophore.in/tools/mdforge): Cloud and GPU-accelerated molecular dynamics simulations powered by OpenMM, Amber14SB/GAFF force fields, explicit TIP3P water solvation, and trajectory analytics (RMSD, RMSF, Rg, H-bond counts).
- [Multi-Vector Lead Prioritization (RankForge)](https://pharmacophore.in/tools/rankforge): Multi-parametric lead ranking combining docking affinity, QED, logP, synthetic accessibility (SAscore), and custom property weights.
- [Structure-Guided Lead Optimization (OptiForge)](https://pharmacophore.in/tools/optiforge): Pocket-guided fragment elaboration, bioisosteric replacement, scaffold hopping, and multi-objective Pareto optimization.
- [Design of Experiments & RSM (DoEForge)](https://pharmacophore.in/tools/doeforge): Statistical design of experiments including Box-Behnken, Central Composite Design (CCD), Full Factorial, Response Surface Methodology (RSM), ANOVA regression, and Derringer desirability optimization.
- [Drug-Excipient Compatibility (DE-Interact)](https://pharmacophore.in/tools/de-interact): Machine learning prediction of chemical and physical drug-excipient interactions, FTIR/DSC shift estimations, and preformulation risk scoring.
- [Lipid-Based Solubility & Oral Bioavailability (Sol_ME)](https://pharmacophore.in/tools/sol-me): Graph neural networks and QSPR models for predicting compound solubility in lipid vehicles (oils, surfactants, co-surfactants) for SEDDS and SNEDDS formulation design.

## Platform Capabilities & Research Workflows

- **Target-to-Hit (T2H)**: Identify hub proteins via NetForge, generate novel chemical matter via PharmForge AI, and screen binding affinity with DockForge.
- **Hit-to-Lead (H2L)**: Optimize binding pose stability with MDForge, rank candidates using RankForge, and perform pocket-guided bioisosteric replacement with OptiForge.
- **Lead-to-Candidate (L2C)**: Formulate lipid nanoparticles or SEDDS with Sol_ME, verify excipient compatibility with DE-Interact, and optimize formulation parameters with DoEForge.

## Key APIs & Data Standards

- **Molecular Formats**: SMILES, InChI, PDB, PDBQT, SDF, MOL2, DCD trajectories.
- **Scientific Force Fields**: OpenMM Amber14SB, GAFF2, TIP3P, CHARMM36.
- **Docking Algorithms**: AutoDock Vina scoring function, Lamarckian Genetic Algorithm.
- **Statistical Modeling**: OLS Regression, ANOVA, Response Surface Optimization.

## Documentation & Legal

- [Open-Source Licences & Third-Party Notices](https://pharmacophore.in/legal/open-source-licences.html)
- [Terms of Service](https://pharmacophore.in/terms)
- [Privacy & Data Consent Policy](https://pharmacophore.in/data-consent)
- [Contact & Support](https://pharmacophore.in/contact)