Stonewise

The company's core offering is an AI-driven drug discovery platform, StoneMIND®, which serves pharmaceutical companies and research institutions. This platform functions as a unified SaaS solution, providing tools for information extraction, knowledge mining, and molecular design. The business model centers on providing these integrated solutions to enhance the efficiency and success rate of early-stage drug discovery for its clients. By integrating AI with computational chemistry, pharmacology, and molecular dynamics, Stonewise aims to make drug development more time-efficient and cost-effective.

The StoneMIND® platform has several key features. Its information extraction system can pull chemical structures from documents and images, allowing researchers to build their own chemical information databases quickly. The Knowledge Discovery in Database (KDD) system facilitates data annotation and analytics for collaborative knowledge sharing. A central component is the molecular design system, which uses multi-layer models to design and optimize molecules based on ligands, receptors, and fragments, supported by a massive database. This system includes capabilities for ultra-high-throughput virtual screening, deep learning-driven scaffold hopping, and predictions for bioactivity and ADMET (absorption, distribution, metabolism, excretion, and toxicity).

Since its inception, Stonewise has achieved significant financial milestones, completing four rounds of financing in 2020 alone. In 2021, the company announced the completion of its Series B and B+ financing rounds, raising a total of $100 million. These funds are intended for talent acquisition and further development of its AI platform. Key investors include Legend Capital, Lightspeed China, Greater Bay Area Community Development Fund, Hillhouse Ventures, and SIG, among others. Keywords: AI drug discovery, small molecule drugs, computational chemistry, pharmaceutical technology, molecular design, drug R&D, StoneMIND, Jielong Zhou, virtual screening, ADMET prediction, knowledge graph, lead optimization, preclinical candidate compounds, scaffold hopping, drug design platform, computational biology, pharmacology, venture capital, Beijing, China