MRlogP: Transfer learning enables accurate logP prediction using small experimental training datasets

Yan-Kai Chen, Steven Shave, Manfred Auer

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of high quality, abundant training data for machine learning methods can be a major limiting factor in building effective property predictors. We utilize transfer learning techniques to get around this problem, first learning on a large amount of low accuracy predicted logP values before finally tuning our model using a small, accurate dataset of 244 druglike compounds to create MRlogP, a neural network-based predictor of logP capable of outperforming state of the art freely available logP prediction methods for druglike small molecules. MRlogP achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP. We have made the trained neural network predictor and all associated code for descriptor generation freely available. In addition, MRlogP may be used online via a web interface.
Original languageEnglish
Article number2029
Number of pages9
Issue number11
Publication statusPublished - 13 Nov 2021

Keywords / Materials (for Non-textual outputs)

  • lipophilicity prediction
  • logP prediction
  • transfer learning
  • physicochemical property prediction


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