AI Article Synopsis

  • Machine learning in chemical screening has improved but still struggles with accuracy in identifying novel compounds, often retrieving known structures instead.
  • This study introduced a method to enhance the evolutionary chemical binding similarity (ECBS) model by using experimental data for iterative optimization, leading to better predictions.
  • The research successfully identified new MEK1 inhibitors with high binding affinities, showcasing the model's effectiveness and the potential for developing new drug-like molecules.

Article Abstract

Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding similarity (ECBS) model using experimental validation data. Various data update and model retraining schemes were tested to efficiently incorporate new experimental data into ECBS models, resulting in a fine-tuned ECBS model with improved accuracy and coverage. To demonstrate the effectiveness of our approach, we identified the novel hit molecules for the mitogen-activated protein kinase kinase 1 (MEK1). These molecules showed sub-micromolar affinity (Kd 0.1-5.3 μM) to MEKs and were distinct from previously-known MEK1 inhibitors. We also determined the binding specificity of different MEK isoforms and proposed potential docking models. Furthermore, using de novo drug design tools, we utilized one of the new MEK inhibitors to generate additional drug-like molecules with improved binding scores. This resulted in the identification of several potential MEK1 inhibitors with better binding affinity scores. Our results demonstrated the potential of this approach for identifying novel hit molecules and optimizing their binding affinities.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517535PMC
http://dx.doi.org/10.1186/s13321-023-00760-6DOI Listing

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