It processes over one thousand interactions per week on cloud infrastructure, making high-precision ligand binding calculations available through a user-friendly interface.
How XFEP Works with High‑Precision Physics and AI‑Driven Modeling
Physics‑Driven Free Energy Perturbation Enables Accurate Binding Predictions
XFEP uses free energy perturbation, a physics‑based method rooted in statistical mechanics, to model how drug molecules bind to target proteins.
This method captures the energy difference between bound and unbound states with quantum‑level accuracy.
XtalPi optimized these calculations through proprietary force fields and refined protocols, enabling highly reliable predictions of ligand‑protein affinity.
XFEP Runs Thousands of Simulations at Cloud Scale
XFEP operates on a scalable cloud infrastructure with elastic GPU clusters. The platform processes over 1,000 binding simulations each week, greatly boosting throughput compared to traditional lab experiments. Scientists access results via a desktop‑style interface that hides the complexity of GPU computing.
Generative AI Proposes Novel Molecules for FEP Testing
XFEP integrates AI modules that propose new candidate molecules based on learning from large chemical databases. The generative models work hand in hand with FEP simulations to iteratively suggest compounds with optimal binding potential. Researchers can expand their chemical exploration without manually designing molecules.
Active Learning Enhances Prediction Accuracy Over Time
The platform applies active learning techniques by continuously refining its AI predictions with fresh FEP simulation results. This feedback loop helps the system update its internal models and force field parameters, improving accuracy for Pfizer's proprietary molecule sets.
XFEP Offers Both Relative and Absolute Binding Energy Predictions
XFEP supports both relative and absolute free energy calculations. Relative FEP compares modifications within a chemical series, while absolute FEP models the full binding energy of unique molecules. This dual capability allows seamless transitions from hit discovery to lead optimization stages.
Custom Force Field Enhances Results for Client Molecules
XtalPi tailors the XFEP platform to client‑specific chemical libraries. For Pfizer, the system adjusts force field parameters and protocols to match the unique structural features of their molecule candidates. This customization improves predictive precision across diverse drug targets
Why This Collaboration Matters for Pharma Companies
This expanded partnership is more than a technical agreement. It signals a paradigm shift in how pharmaceutical companies approach drug discovery.
1. Precision and speed at scale
By combining physics-driven simulations with AI training on proprietary Pfizer data, the platform can dramatically improve hit identification accuracy and reduce computational time. In practice, this means faster iterations and significantly fewer lab experiments, accelerating lead discovery from months to weeks.
2. Enhanced R&D productivity
The ability to simulate thousands of compounds quickly and accurately lightens the load on chemists and biologists. Instead of synthesizing and screening large compound libraries, researchers can zero in on high-confidence candidates. This efficiency reduces costs, shortens timelines, and lets scientists focus on critical experiments and validation.
3. Strategic competitive edge
Pharma firms that integrate AI-first drug design tools early can gain a strategic advantage. As Stefan Hock recently observed, this movement “signals a fundamental shift—moving drug discovery from slow, manual screens to rapid, AI-powered molecular design at unprecedented scale.”
4. Industry validation and ecosystem momentum
XtalPi’s work with giants like Pfizer, Eli Lilly ($250M AI deal), and Johnson & Johnson validates its platform’s broad utility. These partnerships drive a virtuous cycle, attracting more innovation and funding in AI-enhanced drug development.
5. De-risking early-stage research
Free energy calculations supported by AI reduce the risk of late-stage failures. By predicting issues such as off-target effects or poor binding earlier, the platform reduces the likelihood of costly attrition further down the line.
How GenAI Amplifies XFEP’s Quantum-Level Accuracy for Smarter Drug Design
XtalPi’s XFEP platform blends quantum physics and generative AI to revolutionize how drug discovery teams identify promising small-molecule candidates.
The platform uses physics-based free energy perturbation to simulate how molecules interact with protein targets with near-experimental accuracy. Generative AI then proposes and refines novel molecules by actively learning from FEP results.
This tight integration ensures researchers spend less time running simulations and more time innovating. XFEP runs over a thousand cloud-based binding simulations each week, enabling rapid iteration and informed decision-making in early-stage drug discovery
What Comes Next for XFEP in Pharma Research
Pharma companies are preparing to use XFEP across broader chemical collections and in different therapeutic areas. XtalPi plans to integrate automated molecule suggestion loops with real-time binding simulations. They also aim to further refine interfaces for seamless adoption by R&D scientists.
