On failure modes in molecule generation and optimization.

There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested performance metrics or scoring functions often do not cover all relevant aspects of drug design projects. In this work, we highlight some unintended failure modes in molecular generation and optimization and how these evade detection by current performance metrics.

Tales of 1,008 small molecules: phenomic profiling through live-cell imaging in a panel of reporter cell lines.

Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

Disubstituted Bis-THF Moieties as New P2 Ligands in Nonpeptidal HIV-1 Protease Inhibitors (II).

A series of darunavir analogues featuring a substituted bis-THF ring as P2 ligand have been synthesized and evaluated. Very high affinity protease inhibitors (PIs) with an interesting activity on wild-type HIV and a panel of multi-PI resistant HIV-1 mutants containing clinically observed, primary mutations were identified using a cell-based assay. Crystal structure analysis was conducted on a number of PI analogues in complex with HIV-1 protease.

Disubstituted Bis-THF Moieties as New P2 Ligands in Nonpeptidal HIV-1 Protease Inhibitors.

A series of darunavir analogues featuring a substituted bis-THF ring as P2 ligand have been synthesized and evaluated. High affinity protease inhibitors (PIs) with an interesting activity on wild-type HIV and a panel of multi-PI resistant HIV-1 mutants containing clinically observed, primary mutations were identified using a cell-based assay. A number of PIs have been synthesized that show equivalent and greater activity for HIV-1 mutant strains as compared to wild-type HIV-1. The activity on the purified enzyme was confirmed for a selection of analogues.