Publications

Hit Discovery using Docking ENriched by GEnerative Modeling (HIDDEN GEM): A Novel Computational Workflow for Accelerated Virtual Screening of Ultra-large Chemical Libraries

Published in Molecular Informatics, 2023

We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow.

Recommended citation: Wellnitz, J., Popov, K.I., Maxfield, T, and Tropsha, A. (2023), Hit Discovery using Docking ENriched by GEnerative Modeling (HIDDEN GEM): A Novel Computational Workflow for Accelerated Virtual Screening of Ultra-large Chemical Libraries.. Mol. Inf.. Accepted Author Manuscript. https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.202300207

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds

Published in Journal of Medicinal Chemistry, 2023

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed “Liability Predictor,” a free web tool to predict HTS artifacts.

Recommended citation: Alves, V. M., Yasgar, A., Wellnitz, J., Rai, G., Rath, M., Braga, R. C., … Tropsha, A. (2023). Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds. Journal of Medicinal Chemistry, 66(18), 12828–12839. https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482#

The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation

Published in Arxiv Preprint, 2023

We extend these previously defined validation strategies for QSAR modeling of binary mixtures to the more complex case of general, N-ary mixtures and argue that these strategies are applicable to many modeling tasks beyond simple chemical mixtures.

Recommended citation: Travis Maxfield, Joshua Hochuli, James Wellnitz, Cleber Melo-Filho, Konstantin I. Popov, Eugene Muratov, & Alex Tropsha. (2023). The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation. https://arxiv.org/abs/2308.06347

The assembly of β-barrel membrane proteins by BAM and SAM

Published in Molecular Microbiology, 2020

Gram-negative bacteria, mitochondria, and chloroplasts all possess an outer membrane populated with a host of β-barrel outer-membrane proteins (βOMPs)… Here, we will review these recent studies and highlight their contributions toward understanding βOMP biogenesis in Gram-negative bacteria and in mitochondria.

Recommended citation: Recommended citation: Y Lundquist, K, Billings, E, Bi, M, Wellnitz, J, Noinaj, N. The assembly of β-barrel membrane proteins by BAM and SAM. Mol Microbiol. 2021; 115: 425–435. https://onlinelibrary.wiley.com/doi/full/10.1111/mmi.14666