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At SpiroChem, we recognize the critical importance of high-quality, machine-readable data to support AI/ML applications in chemistry. Our electronic lab notebook (ELN) system ensures that all data generated is in a machine-readable format, facilitating seamless integration with AI/ML platforms.
We are pioneering our own algorithmic ecosystem designed to augment the capabilities of our scientists, creating an "augmented chemist" capable of making faster and more informed decisions. We are also developing automation systems to further our vision as a highly integrated drug discovery partner.
While this is an ongoing development, it underscores our commitment to staying at the forefront of technology. We take great pride in our ability to report and analyze data obtained from parallel chemistry efforts, including parallel synthesis for making libraries and high-throughput experimentation (HTE) for optimizing reaction conditions. We feed this data into algorithms to continually refine our processes and outcomes.
Applications of this technology have supported efforts towards generative AI solutions and the design of both retrosynthetic and forward synthesis tools, the latter being leveraged to define chemistry enabled virtual chemical spaces. For more information on our parallel chemistry and HTE capabilities, please refer to the dedicated section here.
