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Following our recent EGFR case study, we’re continuing our computational drug design campaign using the set of 9 FDA compounds we identified as hits using our IC50 Predictions Engine in Case Study 1. In this article, we delve deeper into the methodologies and computational tools that are utilized for optimizing hit compounds identified in high-throughput screening (HTS). We'll showcase how our Selective Mutation Generator, Characterization Dashboard, and Solubility Dashboard significantly enhance compound evaluation, lead optimization, and the Design-Make-Test-Analyze (DMTA) cycle.

Revisiting EGFR: A Proven Benchmark

Our previous EGFR-focused case study highlighted the strength of Revilico's Discovery Engine, which accurately ranked FDA-approved EGFR inhibitors among the top performers in a large dataset of 125,000 compounds. We were able to identify potent binding affinity and inhibition of EGFR, as our compounds performed in the top 3% of performers, an expected outcome experimentally. These results validated our AI model’s ability to robustly predict and identify high-affinity therapeutic candidates.

Molecular Evolution via the Selective Mutation Generator

Moving beyond hit identification, compound optimization becomes crucial. Our Selective Mutation Generator systematically introduces strategic chemical variations to promising hits while maintaining molecular similarity and therapeutic intent. This powerful tool provides a controlled exploration of chemical space through customizable mutation options, including functional group addition and replacement, halogenation, chain and ring modifications, stereochemistry inversion, and a variety of other methods.

Using the nine FDA-approved EGFR inhibitors identified earlier, we applied selective mutations to generate novel analogs. This sophisticated molecular editing process yielded chemically valid and structurally diverse derivatives, providing a valuable expansion of our compound dataset for further optimization.

Comprehensive Molecular Profiling with the Characterization Dashboard

Post-mutation, the Characterization Dashboard plays a pivotal role by enabling rapid, large-scale assessments of chemical properties. This dashboard evaluates key physicochemical parameters including solubility, permeability, chemical stability, molecular weight, and structural characteristics. Specifically, the dashboard handles datasets of hundreds of thousands of compounds, classifying and segmenting them based on predictive ADME profiles. The resulting classifications facilitate easy identification and selection of optimal candidates for advancement, thereby significantly enhancing the decision-making process in early drug discovery phases, and enabling medicinal chemists to make data informed decisions on compound synthesis and testing.

Solubility Dashboard: Precision in ADME Profiling

Solubility is a fundamental determinant of a drug’s success, directly impacting bioavailability, formulation strategies, and clinical outcomes. Revilico’s Solubility Dashboard addresses this challenge through precise computational predictions of solubility at various physiological pH levels. This is very important for orally bioavailable small molecule drugs that require varying levels of solubility at pH 7.4 versus pH of 2-4.5 in more acidic environments after ingestion.

This tool evaluates critical solubility metrics such as log P (partition coefficient), log D (distribution coefficient), and log S (aqueous solubility). It graphically represents solubility profiles across a physiologically relevant pH spectrum (1–8), helping researchers predict oral bioavailability and stability across different physiological environments. For our EGFR derivatives, this allowed us to pinpoint compounds most suitable for oral drug delivery and potential clinical application.

An Integrated Optimization Pipeline

Integrating these three powerful computational tools has allowed us to create an advanced optimization pipeline, markedly reducing the time and resources typically required in traditional drug discovery workflows. This integrated approach combines chemical mutation, comprehensive characterization, and precise solubility assessment to yield a highly refined set of optimized lead candidates.

Next Steps: Atomistic Docking and Beyond

As we continue optimizing the EGFR inhibitors, our next steps involve sophisticated atomistic docking studies to further validate and confirm ligand-protein interactions. By integrating this structural validation with our chemical profiling data, we can refine the pool of candidates further, advancing compounds that demonstrate optimized physiochemical properties, exceptional bioavailability, and robust target affinity. Our iterative approach ensures that only the most promising drug candidates proceed through the DMTA cycle, significantly accelerating drug development timelines, reducing costs, and enhancing overall therapeutic potential.

Join Our Community and Webinar

To learn more and see these optimization tools in action, join our upcoming webinar on April 2nd at 12pm PST. Engage directly with our expert team, ask questions, and discover how Revilico is driven drug discovery forward through computational innovation.

• Register for our Webinar April 2nd at 12PM PST: Sign up here

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