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BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity.

Ferruccio Palazzesi1, Markus R Hermann1, Marc A Grundl1

  • 1Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstrasse 65, 88397 Biberach an der Riß, Germany.

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This study introduces BIreactive, a novel computational method combining quantum mechanics and machine learning to accurately and rapidly predict the reactivity of covalent drugs. This tool aids in accelerating the discovery of potent covalent therapeutics.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Covalent drugs offer advantages like higher potency and prolonged target inhibition compared to noncovalent drugs.
  • The reactivity of covalent drugs is determined by their 'warhead,' a functional group forming a bond with the target protein.
  • Accurate prediction of covalent compound reactivity is crucial for efficient drug discovery.

Purpose of the Study:

  • To develop a computational method that precisely estimates the intrinsic reactivity of covalent compounds.
  • To combine the accuracy of ab initio calculations with the speed of machine learning for reactivity prediction.
  • To introduce a tool that can be applied to various covalent warhead classes and molecular sizes.

Main Methods:

  • Developed 'BIreactive,' a hybrid approach using ab initio activation energies and machine learning.
  • Applied BIreactive to acrylamides and 2-chloroacetamides, representing different reaction mechanisms.
  • Integrated BIreactive with a truncation algorithm to enable analysis of both fragments and lead-like molecules.

Main Results:

  • BIreactive accurately estimates transition state barriers and intrinsic reactivity.
  • The method demonstrates applicability across different covalent warhead classes.
  • BIreactive successfully predicts reactivity for both small fragments and larger lead-like molecules.

Conclusions:

  • BIreactive offers a fast and accurate method for predicting covalent compound reactivity.
  • This approach significantly enhances the drug discovery process for covalent therapeutics.
  • The generic nature of BIreactive allows for broad application in pharmaceutical research involving covalent drugs.