Pauling

Pauling.AI is the first fully autonomous drug discovery platform, designed to take researchers from a research idea to validated drug molecules in days rather than years. By combining AI agents, enterprise-scale cloud infrastructure, and the latest AI models with traditional computational chemistry tools, Pauling.AI eliminates the need for computational chemistry expertise — scientists simply describe their protein target through a conversational interface and the platform handles the rest.

The platform addresses a fundamental problem in the industry: most AI drug discovery tools are either single-purpose models or require deep computational expertise, forcing scientists to spend time learning complex software instead of doing science. With drug development averaging 10–15 years per drug, costing approximately $2.6 billion per approval, and carrying a 90% failure rate, Pauling.AI offers a compelling alternative through complete in silico automation. The platform is patent pending.

How the Autonomous Workflow Operates

• Researchers provide a protein target (such as a PDB ID like "6VC8") through a conversational interface — no computational chemistry knowledge required
• The AI agent automatically selects optimal protocols, determines simulation conditions, and configures the complete computational pipeline
• Millions of compounds are processed in parallel using auto-scaling cloud infrastructure with fault-tolerant, restartable pipelines
• Automated quality control is performed using MolProbity analysis and PoseBusters validation to ensure only high-quality, drug-like molecules advance
• ADMET and safety profiling predicts 80+ drug properties — including absorption, metabolism, toxicity, and off-target effects — before synthesis to reduce costly late-stage failures
• Advanced Molecular Dynamics (MD) simulations validate top hits with binding affinity estimation and stability analysis to provide confidence in selected compounds

15+ Integrated Pipelines

• Receptor preparation and protonation
• Large-scale, state-of-the-art molecular docking
• Integration of the latest AI models including Boltz and DiffDock
• Molecular dynamics and binding affinity estimation via GROMACS
• ADMET property prediction across 80+ endpoints
• Automated quality control and validation throughout the workflow

Enterprise Cloud Infrastructure

• Capable of processing millions of compounds in parallel
• Auto-scaling cloud resources to meet research demands
• Fault-tolerant execution with restartable job management
• Secure data handling throughout the pipeline
• Seamless data flow between all computational tools with no manual file uploads, downloads, or format conversions required

Multi-LLM AI Agent Capabilities

• Conversational interface for intuitive target input and workflow management
• Expert-level protocol selection guided by biological context
• pH optimization tailored to specific targets
• Multi-model AI support with continuous learning
• Automatic parameter optimization based on target and molecular properties — no manual configuration needed

Competitive Advantages Over Alternatives

• vs. Point solutions (AlphaFold, Boltz, etc.): Pauling.AI delivers end-to-end automation rather than single-purpose tools that still require manual integration
• vs. Complex tools (Schrödinger, etc.): No computational chemistry expertise or weeks of learning required; the AI agent guides the entire workflow autonomously
• vs. CRO services: Results in hours to days rather than weeks to months, with full transparency into methodology rather than a proprietary black box
• Supports millions of compounds at enterprise scale, overcoming throughput and licensing limitations common in alternative platforms
• Latest AI models such as Boltz and DiffDock are integrated and automated, not requiring manual setup or complex configuration

Pauling.AI is trusted by pioneering researchers and supported by leading organizations committed to revolutionizing drug discovery through AI. The platform is purpose-built for medicinal chemists and research teams who want to focus on biology and science — not software — enabling faster, more cost-effective paths from idea to validated drug candidate.