Pharmacophore Modeling

Pharmacophore modeling aims to identify the essential 3D arrangement of functional groups crucial for a molecule's biological activity, accelerating drug discovery. Current research emphasizes developing efficient algorithms, such as neural networks and graph-matching techniques, to rapidly screen vast chemical libraries and generate novel molecules with desired pharmacophore features. This approach leverages machine learning to improve the speed and accuracy of virtual screening and de novo drug design, ultimately streamlining the identification of promising drug candidates. The integration of experimental data, particularly incorporating both positive and negative results, is increasingly recognized as crucial for building robust and interpretable models.