A new submolecular quantitative structure activity relationship (QSAR) descriptor was applied toward elucidating the anti-malarial pharmacophore of tryptanthrins, a class of compounds known for their anti-parasitic activities. The new descriptor is based on experimental and computational measurements of the tunneling barriers of individual lobes of the molecular orbitals. Lobe-by-lobe QSAR correlation plots revealed a single lobe of the LUMO to be strongly associated with tryptanthrin's anti-malarial activity. The correlation also showed a threshold behavior wherein barriers below a particular value show low IC values. Above the threshold, the correlation of IC vs the logarithm of the barrier is linear with R = 0.999. This barrier threshold may be applied as a design criterion for future tryptanthrin-based anti-malarial lead optimization. The new descriptor may be broadly applicable toward other molecular systems of interest, such as catalysts, pesticides, and herbicides. The authors have named the new descriptor: submolecular tunneling analysis of barriers (STAB).
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http://dx.doi.org/10.1016/j.jmgm.2017.12.013 | DOI Listing |
MAbs
December 2025
Department of Purification, Microbiology and Virology, Genentech Inc, South San Francisco, CA, USA.
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purification development often conflicts with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models that predict monoclonal antibody chromatographic binding behavior from the amino acid sequence.
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December 2024
School of Computing and Data Sciences, FLAME University, Pune, India.
This study illustrates the use of chemical fingerprints with machine learning for blood-brain barrier (BBB) permeability prediction. Employing the Blood Brain Barrier Database (B3DB) dataset for BBB permeability prediction, we extracted nine different fingerprints. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were used to develop models for permeability prediction.
View Article and Find Full Text PDFSci Rep
January 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.
View Article and Find Full Text PDFACS Omega
December 2024
Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
This study introduces an innovative computational approach using hybrid machine learning models to predict toxicity across eight critical end points: cardiac toxicity, inhalation toxicity, dermal toxicity, oral toxicity, skin irritation, skin sensitization, eye irritation, and respiratory irritation. Leveraging advanced cheminformatics tools, we extracted relevant features from curated data sets, incorporating a range of descriptors such as Morgan circular fingerprints, MACCS keys, Mordred calculation descriptors, and physicochemical properties. The consensus model was developed by selecting the best-performing classifier-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), or Support Vector Machines (SVM)-for each descriptor, optimizing predictive accuracy and robustness across the end points.
View Article and Find Full Text PDFJ Mol Graph Model
March 2025
Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh,11623, Saudi Arabia. Electronic address:
The work being presented now combines severe gradient boosting with Shapley values, a thriving merger within the field of explainable artificial intelligence. We also use a genetic algorithm to analyse the HDAC1 inhibitory activity of a broad pool of 1274 molecules experimentally reported for HDAC1 inhibition. We conduct this analysis to ascertain the HDAC1 inhibitory activity of these molecules.
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