Dataset Information

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

ABSTRACT: In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

SUBMITTER: Horne RI

PROVIDER: S-EPMC10865343 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

Horne Robert I RI Wilson-Godber Jared J González Díaz Alicia A Brotzakis Z Faidon ZF Seal Srijit S Gregory Rebecca C RC Possenti Andrea A Chia Sean S Vendruscolo Michele M

Journal of chemical information and modeling 20240123 3

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen ta ...[more]

PMID: 38261763

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Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT, RDX and HMX. One important goal of microarray experiments is to discover novel biomarker genes for quantitative phenotypic prediction. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can be used to quantitatively predict earthworm tissue residues of the explosives compounds that they were exposed to and took in from the HMX-spiked soil. Results: We collected a large microarray gene expression and earthworm tissue residue dataset. First, differentially expressed genes were identified for each exposure duration (4, 14 and 28 days). These genes were used in multivariate regression modeling for HMX residue prediction. Eighteen different regression models were tested and compared. The best performing model was able to achieve very high prediction accuracies with R2 values of 0.715, 0.728 and 0.822 for 4 days, 14 days and 28 days exposures, separately. Conclusions: This study demonstrated that multivariate regression coupled with high throughput microarray gene expression was a promising approach to quantitative phenotypic prediction. Adult earthworms (Eisenia fetida) were exposed in soil spiked with HMX (0, 8, 16, 32, 64, or 128 mg/kg) for 4, 14 or 28 days. Each treatment originally had 10 replicate worms with all 10 worms survived at the end of exposure. Total RNA was isolated from the surviving worms. A total of 120 worm RNA samples were hybridized to a custom-designed oligo array using AgilentM-bM-^@M-^Ys one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript. After hybridization and scanning, gene expression data were acquired using AgilentM-bM-^@M-^Ys Feature Extraction Software (v.9.1.3). The 120-array dataset consists of three exposure groups (4 day, 14 day and 28 day) with each group having the following 8 treatments: day 0 control, blank control, solvent control, 8, 16, 32, 64, and 128 mg HMX/g soil. Each treatment had 5 biological replicates (i.e., five individual worms).

Dataset Information

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

Publications

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

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