Pending AI Platform

Pending AI is a next-generation computational drug discovery platform that seamlessly integrates scalable quantum mechanics with artificial intelligence to design higher-quality medicines. Built for pharmaceutical researchers and drug discovery teams, the platform pushes the boundaries of traditional approaches by targeting diseases that were previously considered intractable, delivering quantum-level accuracy at a fraction of the cost and time of conventional methods.

The platform is structured around three scientific pillars — Quantum-Based Structural Biology, Artificial Intelligence-Guided Drug Libraries, and Drug Profile Optimization — unified into a single end-to-end engine. A bioinformatics-driven approach underpins the entire workflow, identifying the most promising disease targets amenable to quantum mechanical improvements and providing a truly differentiated starting point for discovery.

Quantum-Based Structural Biology

• Quantum mechanical calculations are applied to experimental datasets from x-ray crystallography and cryo-electron microscopy (electron density maps) to generate higher-quality crystal structures of disease targets.
• Structure quality is confirmed through crystallographic benchmark metrics such as clashscores and the mapping of physically correct non-covalent interactions.
• High-performance computing clusters and bespoke divide-and-conquer techniques enable quantum-level accuracy for traditionally intractable protein structures.
• AI models trained on these quantum mechanical calculations maintain quantum-level accuracy while requiring a thousand-fold fewer computational resources in terms of both cost and time.

Artificial Intelligence-Guided Drug Libraries

• Ultra-high-throughput generative AI capabilities are used to build trillion-scale virtual drug libraries, exploring pharmaceutical space at an unprecedented scale.
• Generated molecules are benchmarked against gold-standard pharmacological criteria including drug-likeness, diversity, novelty, and synthetic accessibility.
• Absorption, distribution, metabolism, and excretion (ADME) properties are evaluated as part of the benchmarking process.
• The library enables similarity-distance searching across more than one trillion novel drug candidates.

Drug Profile Optimization

• Top candidate molecules undergo iterative optimization through similarity searching, reinforcement learning, and transfer learning techniques.
• Quantum mechanical interaction modelling is incorporated into the optimization feedback loop.
• This process enables fine-tuning of key drug properties including binding affinity and target inhibition efficacy.
• The platform is capable of identifying both first-in-class and best-in-class drug candidates.

Platform Scale and Architecture

• Approximately 85% of the human proteome is considered undruggable by traditional methods — Pending AI's approach is designed to address this gap.
• The hybrid data-driven and physics-informed platform delivers a 1,000-fold reduction in the cost and time of quantum mechanical calculations.
• Trillion-scale virtual screening libraries are tailored to maximise discovery potential.
• The platform provides end-to-end integration from target selection through to lead optimization.
• AI and quantum mechanical workloads run on public or private clouds, as well as on high-performance computing clusters, making the platform cloud-native and highly scalable.

Pending AI holds ISO 27001 certification and has undergone SOC 2 auditing, reflecting a strong commitment to cybersecurity best practices and data security for its partners and collaborators.