Predictive Minisci and P450 Late Stage Functionalization with Transfer Learning

Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made significant strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms

Predictive Minisci late stage functionalization with transfer learning

Abstract Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms

Predictive Minisci late stage functionalization with transfer learning

Acknowledgements: Financial support for this work was generously provided by Pfizer and the Royal Society (Newton International Fellowship to E.K.S. and University Research Fellowship to A.A.L.). We wish to thank Rokas Elijošius, William McCorkindale, and Oliver P. King-Smith for their enlightening discussions. We are grateful to Hans Renata and Roger M. Howard for assistance in manuscript preparation. The authors would like to acknowledge several Pfizer colleagues and Spectrix vendor partners who have contributed to this work including Manjinder Lall, Gregory Walker, R. Scott Obach, and Douglas Spracklin for their leadership and execution of the Lead Diversification Platform (LDP), and Danial Morris for LDP product generation, isolations, bioanalytical support, and anyone else who has contributed to the LDP from the date of its inception.Funder: Royal Society; doi: https://doi.org/10.13039/501100000288Funder: Pfizer (Pfizer Inc.); doi: https://doi.org/10.13039/100004319Funder: Spectrix Analytical LLCStructural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms

Predictive Minisci late stage functionalization with transfer learning.

Acknowledgements: Financial support for this work was generously provided by Pfizer and the Royal Society (Newton International Fellowship to E.K.S. and University Research Fellowship to A.A.L.). We wish to thank Rokas Elijošius, William McCorkindale, and Oliver P. King-Smith for their enlightening discussions. We are grateful to Hans Renata and Roger M. Howard for assistance in manuscript preparation. The authors would like to acknowledge several Pfizer colleagues and Spectrix vendor partners who have contributed to this work including Manjinder Lall, Gregory Walker, R. Scott Obach, and Douglas Spracklin for their leadership and execution of the Lead Diversification Platform (LDP), and Danial Morris for LDP product generation, isolations, bioanalytical support, and anyone else who has contributed to the LDP from the date of its inception.Funder: Royal Society; doi: https://doi.org/10.13039/501100000288Funder: Pfizer (Pfizer Inc.); doi: https://doi.org/10.13039/100004319Funder: Spectrix Analytical LLCStructural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms