SciMind Entrance

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Artificial General Intelligence represented by large models is sweeping in, gradually affecting all aspects of work and life. However, the three major challenges of high cost, long cycle, and low success rate in the field of drug research and development still weigh on us. In order to better cope with these challenges, we are excited to introduce to you an exciting technical innovation - a brand new partner for drug research and development, AI Assistant SciMind, a large-scale multi-modal mixture of experts system born for the biomedical field.

🌐 The Perfect Convergence of Foundation Models and Drug Research and Development

ChatGPT has sparked public expectations for foundation models, but foundation models consume big and high quality data, and data in the biomedical field is scarce, with low utilization rates. The data and models are stuck in a vicious cycle, resulting in even the most advanced existing large models, such as GPT4, being unable to effectively handle molecular tables, chemical molecules, and biological sequences and other information in biomedical documents. Based on the world’s leading chemical information extraction tool αExtractor, we automatically structure the information, including molecular images, in documents and patents with the state-of-the-art accuracy. Based on pharmaceutical big data, we build a multi-modal large model for pharmaceutical documents.

🔬 Freer Retrieval and Q&A

The speed at which literature and patents are produced has now exceeded the limits of what the human brain can handle. Simple filtering through search engines and presentation through knowledge graphs can no longer meet the requirements. Moreover, the massive internal documents produced by pharmaceutical companies and research institutions also urgently need to be better archived and utilized. SciMind, a more flexible document summarization and knowledge Q&A system, will help to understand the current state of research and not miss the latest research results.

🚀 More Powerful Molecular Design Toolchain

Too many parameters in drug design tools to use? SciMind integrates the most advanced molecular property prediction, molecular generation, and structure optimization tools. Just state your needs, and you, who are most familiar with the project and have the most chemical intuition, can use those tools easily and controllably, without “middlemen” such as an CADD researcher.

Now, let’s delve into the widely applicable usage scenarios, multi-dimensional capability evaluations, and how to apply for a trial, and jointly reveal this technological miracle that leads drug research and development!

Key Features

Chart Recognition: One of the primary capabilities of the SciMind is its powerful chart recognition ability, which can quickly extract key data from complex literature.

Peptide Sequence Data Extraction: To meet the specific needs of the peptide research field, SciMind can accurately identify and extract peptide sequence data from patents or literature.

Markush Structure Analysis: In the analysis of patents in the chemical and biomedical fields, SciMind provides automatic parsing of Markush structures and determination of patent protection scope.

Drug Target Discovery: SciMind can realize the retrieval of disease, target, and drug, whether it is the potential target of disease, potential indication of target, potential drug of disease, drug safety, etc. Complex recommendations such as target recommendations for weight loss only in the abdomen and not in the chest, can provide referential information to prioritize targets.