Company: SHPH
Filing Date: 2025-12-11
Form Type: S-1/A
Source: 0001493152-25-027310
Chunk: 4

Company: Shuttle Pharmaceuticals Holdings, Inc.
Filing Date: 2025-12-11
Form: S-1/A
Chunk 4
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 drug development, were acquired by a wholly owned
subsidiary of the Company. By combining modern AI techniques with structured scientific workflows, the Molecule.ai platform (hereafter,
“Molecule.ai” or the “platform”) helps researchers explore the chemical space more efficiently, evaluate molecular
ideas with greater clarity and make more informed decisions during the earliest stages of drug development. The platform is engineered
to accelerate the iteration cycles that characterize modern drug discovery while preserving scientific reproducibility, traceability
and operational reliability. Molecule.ai adapts state of the art AI algorithms to create a practical, domain-specific AI infrastructure
layer for molecular research and development. The acquisition seeks to leverage Molecule.ai’s molecular modeling and predictive
analytics platform to significantly augment our drug discovery and development business purpose. In tandem with the Molecule.ai asset
acquisition, on November 20, 2025, the Company committed to a plan to discontinue its clinical trials of Ropidoxuridine (the “Clinical
Trials”), our lead product candidate.

Molecule.ai is built on
three core architectural components: a unified inference engine, an API-first integration layer and a modular model framework. The unified
inference engine orchestrates model execution and multi-step reasoning through a deterministic and traceable sequence of operations.
Molecule.ai uses an API-first design, which means that all platform capabilities can be accessed programmatically. All predictive and
reasoning functions are modular, which allows the platform to expand over time without changing the underlying infrastructure. Molecule.ai
currently supports three scientific and computational functions that reflect both its pharmaceutical focus and the structured inference
techniques seen in modern agentic LLM systems: (1) molecular property prediction, (2) cross-molecule and cross-property evaluation and
(3) prediction reasoning and structured molecular insights. The platform predicts a wide range of molecular properties that are relevant
to early-stage discovery and medicinal chemistry and provides inference pipelines for predicting molecular properties. By using transformer-based
models, the platform computes predictive outputs on a wide range of therapeutic tasks. The platform evaluates multiple molecules across
multiple properties in a unified workflow, helping researchers quickly identify the most-promising candidates, understand trade-offs,
and make structured, evidence-based decisions. Molecule.ai also includes a reasoning module that uses LLM-based structured inference
to contextualize predictions, explain differences between compounds, perform rule-guided reasoning and produce narrative or structured
scientific interpretations with the goal to make complex scientific outputs understandable and actionable for broader research and development