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A Fast Ab Initio Predictor Tool for Covalent Reactivity Estimation of Acrylamides
Ferruccio Palazzesi1, Marc A Grundl1, Alexander Pautsch1
1Medicinal Chemistry , Boehringer Ingelheim Pharma GmbH & Co. KG , Birkendorfer Strasse 65 , 88397 Biberach an der Riss , Germany.
Journal of Chemical Information and Modeling
|June 28, 2019
Summary
This study shows how to estimate covalent drug reactivity using the electrophilicity index. A new algorithm helps predict reactivity for larger drug molecules, aiding in safer covalent drug design.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Drug Design
Background:
- Covalent drugs offer high potency due to strong covalent bond formation with biological targets.
- Balancing compound reactivity is crucial to ensure target specificity and minimize off-target effects.
- Acrylamides are a prominent class of covalent warheads used in drug development.
Purpose of the Study:
- To evaluate the electrophilicity index as a predictor of covalent compound reactivity.
- To develop a method for accurately assessing reactivity in lead-like covalent molecules.
- To support the rational design of covalent drugs with optimized reactivity profiles.
Main Methods:
- Application of the electrophilicity index concept to estimate covalent compound reactivity.
- Testing the approach on acrylamide compounds, a common covalent warhead class.
- Development of a truncation algorithm for reactivity calculations in molecules exceeding 250 Da molecular weight.
Main Results:
- The electrophilicity index accurately estimates reactivity for small covalent compounds (MW < 250 Da).
- A novel truncation algorithm ensures correct reactivity estimation for lead-like molecules (MW > 250 Da) by localizing molecular orbitals.
- Caution is advised when using the electrophilicity index for nonterminal acrylamides.
Conclusions:
- The electrophilicity index, especially with the developed algorithm, is a valuable tool for predicting covalent drug reactivity.
- This computational method supports the design of potent and selective covalent inhibitors.
- The approach offers a computationally efficient strategy for advancing covalent drug discovery.
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