Synthetic Molecule Analysis Platform

To tackle this, we're leveraging similarity search algorithms and large language models (LLMs). Basically, we're comparing synthetic compounds to huge datasets of natural molecules. By finding similarities, we can group synthetic molecules with natural "families," helping us predict how they might behave or be used. Similarity search lets us sift through massive amounts of chemical data to find compounds that resemble our synthetic targets. LLMs, like advanced AI language tools, help us interpret this data and make sense of complex chemical information. By combining these technologies, we can interpolate large datasets to discover natural analogues of synthetic molecules. This means we can provide more credible insights into their potential uses and behaviors, grounded in existing natural data. This approach helps address concerns about synthetic molecules by showing how they relate to known compounds. It can lead to breakthroughs in medicine, agriculture, and materials science by predicting possible applications and effects. In a nutshell, we're using advanced search algorithms and AI to bridge the gap between synthetic and natural compounds. This not only boosts the credibility of synthetic molecules but also unlocks their potential in various fields.