Significance: Using computational methodologies, we refined the binding modes of the transient receptor potential cation channel subfamily V member 1 (TRPV1) modulators, capsaicin and resiniferatoxin, provided by the recent experimental cryo-electron microscopy electron density. The resulting insights enable us to predict the binding pose of 96 additional TRPV1 agonists, which we compare with reported mutagenesis studies. Specifically, we characterize the response of five previously unidentified mutants to capsaicin and resiniferatoxin. Analysis of the amino acids engaged in favorable ligand–channel interactions defines the key structural determinants of the TRPV1 vanilloid binding site.

In drug discovery and development, it is crucial to determine which conformers (instances) of a given molecule are responsible for its observed biological activity and at the same time to recognize the most representative subset of features (molecular descriptors). Due to experimental difficulty in obtaining the bioactive conformers, computational approaches such as machine learning techniques are much needed. Multiple Instance Learning (MIL) is a machine learning method capable of tackling this type of problem. In the MIL framework, each instance is represented as a feature vector, which usually resides in a high-dimensional feature space. The high dimensionality may provide significant information for learning tasks, but at the same time it may also include a large number of irrelevant or redundant features that might negatively affect learning performance. Reducing the dimensionality of data will hence facilitate the classification task and improve the interpretability of the model.

Clotrimazole (CT) is a poorly soluble antifungal drug that is most commonly employed as a topical treatment in the management of vaginal candidiasis. The present work focuses on a formulation approach to enhance the solubility of CT using cyclodextrin (CD) complexation. A CT–CD complex was prepared by a co-precipitation method. Various characterization techniques such as differential scanning calorimetry, infrared (IR) and X-ray spectroscopy, scanning electron microscopy and nuclear magnetic resonance (NMR) spectroscopy were performed to evaluate the complex formation and to understand the interactions between CT and CD. Computational molecular modeling was performed using the Schrödinger suite and Gaussian 09 program to understand structural conformations of the complex. The phase solubility curve followed an A_L-type curve, indicating formation of a 1:1 complex. Molecular docking studies supported the data obtained through NMR and IR studies. Enthalpy changes confirmed that complexation was an exothermic and enthalpically favorable phenomenon. The CT–CD complexes were formulated in a gel and evaluated for release and antifungal activity. The in vitro release studies performed using gels demonstrated a sustained release of CT from the CT–CD complex with the complex exhibiting improved release relative to the un-complexed CT. Complexed CT–CD exhibited better fungistatic activity toward different Candida species than un-complexed CT.