Impurity Prediction

Impurity Prediction

Controlling impurities is an essential part for risk management plan in drug development. It involves identifying and categorizing potential impurities in the initial stages of drug discovery.

This process requires extensive manual analysis by chemists, which is time-consuming and labor-intensive. To accelerate drug discovery and production, the implementation of a streamlined impurity analysis method is essential.

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Impurity Prediction Examples

To evaluate the effectiveness of the ChemAIRS impurity prediction model, we selected a literature from Organic Process Research & Development (ORPD) which discussed the synthetic process and impurity studies of the tachykinin receptor antagonist TKA731. One of the synthetic processes was BOC group deprotection, which gave satisfactory yield only on a small scale. With a larger reaction scale, the yield reduced significantly to 50%. When further addressing the impurities, researchers found that the nitrogen atoms in the deprotected pyrrolidine attacked its amide carbonyl terminal under acidic conditions, resulting in impurity 1 formed by intramolecular cyclization and impurity 2 formed by intermolecular coupling. Structure elucidation of these types of impurities often requires purification and further characterization like NMR. Nevertheless, with AI algorithms in ChemAIRS, potential impurity structures and formation routes are provided within minutes, saving valuable research times.

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Assist Chemists in Elucidating the Structures of Impurities Formed

During Chemical Reactions

Supporting Impurity Prediction

ChemAIRS has reached the skill level comparable to that of a highly experienced chemist. It has effectively predicted impurities in common organic reactions with accuracy exceeding 90%.

Boosting Problem-Solving

With AI algorithms, potential impurity structures and formation routes are provided within minutes. Thus, significantly enhancing research efficiency and reducing the time and resources required for structural elucidation.

Using AI algorithms, ChemAIRS accelerates research by rapidly providing insights into impurity structures and formation pathways, enhancing efficiency in both pharmaceuticals and academia sections.