Molecular Optimization Model with Patentability Constraint


  • Sally Turutov Technion - Israel Institute of Technology
  • Kira Radinsky Technion - Israel Institute of Technology



APP: Natural Sciences, APP: Humanities & Computational Social Science


In drug development, molecular optimization is a crucial challenge that involves generating novel molecules given a lead molecule as input. The task requires maintaining molecular similarity to the original molecule while simultaneously optimizing multiple chemical attributes. To aid in this process, numerous generative models have been proposed. However, in practical applications, it is crucial for these models not only to generate novel molecules with the above constraints but also to generate molecules that significantly differ from any existing patented compounds. In this work, we present a multi-optimization molecular framework to address this challenge. Our framework trains a model to prioritize both enhanced properties and substantial dissimilarity from patented compounds. By jointly learning continuous representations of optimized and patentable molecules, we ensure that the generated molecules are significantly distant from any patented compounds while improving chemical properties. Through empirical evaluation, we demonstrate the superior performance of our approach compared to state-of-the-art molecular optimization methods both in chemical property optimization and patentability.




How to Cite

Turutov, S., & Radinsky, K. (2024). Molecular Optimization Model with Patentability Constraint. Proceedings of the AAAI Conference on Artificial Intelligence, 38(1), 257-264.



AAAI Technical Track on Application Domains