Machine learning-based prediction of bioactivity in HIV-1 protease: insights from electron density analysis

<b>Aim:</b> To develop a model for predicting the biological activity of compounds targeting the HIV-1 protease and to establish factors influencing enzyme inhibition.<b>Materials & methods:</b> Machine learning models were built based on a combination of Richard Bader's theory of Atoms in Molecules and topological analysis of electron density using experimental x-ray 'protein-ligand' complexes and inhibition constants data.<b>Results & conclusion:</b> Among all the models tested, logistic regression achieved the highest accuracy of 0.76 on the test set. The model's ability to differentiate between less active and highly active classes was relatively good, as indicated by an AUC-ROC score of 0.77. The analysis identified several critical factors affecting the biological activity of HIV-1 protease inhibitors, including the electron density contribution of hydrogen atoms, bond-critical points and particular amino acid residues. These findings provide new insights into how these molecular factors influence HIV-1 protease inhibition, emphasizing the importance of hydrogen bonding, glycine's flexibility and hydrophobic interactions in ligand binding.