MM/GBSA & Machine Learning

Biolexis Therapeutics MolecuLern Platform

Recent advances in AI/ML coupled with Biolexis's internally developed, fully automated MolecuLern Workflow helping this shift, accelerating, and improving R&D activities of our company's pipeline and some of our collaborator's programs.  Recent advances in high computing, the availability of proven computational algorithms, large, validated data set ML training models, and deep neural networks stemmed an exceptional speed in the field of drug discovery and development. Biolexis MolecuLern platform reduces the timeline and developing costs while aiming for a greater probability of success targeting the CBLB TKB domain for novel leads under this CACHE challenge #4 with the following approaches:

1. MolecuLern is a proprietary structure-embedded platform that uses high-quality half-a-million biochemical lab data (empirical IC50s, Ki, and Kd) from the training set against the vast chemical space of the chosen virtual library and identifies pre-hits with predicted IC50s, Ki, and Kd.

2. Our high-quality wet lab/empirical database to pre-hits data predictions assists further in developing newer AI/ML predictive models against a given CBLB TKB domain within our hit-finding discovery processes.

3. MolecuLern further uses biochemical/mutational data specific to sensitive AAs within defined active site pockets by considering crystallographic to physiological conformational state of hot spots and computes pocket energetics through local dynamic simulations ready for large-scale virtual screening.

4. The platform includes Physics-based relative binding free energy (RBFE) plus MM/GBSA predicted free energy of binding (FEB) of the hits as the penultimate step in the hit selection process.

5. Hybrid of MM/GBSA/Machine Learning & MolecuLern simultaneously predicts PhysicoChem, ADME-Toxicity, in vivo PK, %F and validates the developability candidate's criteria in advance for an easy selection and nomination of candidate-ready at the in vitro stage of screening.

6. MolecuLern significantly reduces the Hit2-Lead optimization timeline due to the embedded empirical IC50s, Ki, and Kd data from the training library against the big library of over 5.0 plus billion libraries.

Biolexis is the first company to combine curated historical wet-lab data, including half a million target-binding data points, with an automated AI/ML-augmented discovery MolecuLern platform. The decades of work and data from our own candidates' non-clinical and clinical data of real-world libraries enabled us to ensure that the chemical space was unique to map the large virtual library synthetically on demand and therefore IP rich. In addition to using MolecuLern to develop our own pipeline assets, Biolexis is working with pharmaceutical and biotech companies through partnerships and academic labs to accelerate the discovery and development of novel small molecule candidates. Due to the use of a real/wet lab-driven MolecuLern process, our approach clearly stat out from the existing computational and or other AIML methods.

MolecuLern

GOLD

MolSoft's ICM

Schrodinger 

Rosetta

DiffDock score plus consensus scores from commercial software to MolecuLern score as criteria for the hit-selection process.

1. Ligand-based Discovery of Novel Small Molecule Inhibitors of RON Receptor Tyrosine Kinase. Mol Inform. 2022, 41 (1), e2000181

2. The novel reversible LSD1 inhibitor SP-2577 promotes anti-tumor immunity in SWItch/Sucrose-NonFermentable (SWI/SNF) complex mutated ovarian cancer. PLoS One. (2020); 15 (7), e0235705. 

3. Repurposing of Proton Pump Inhibitors as First Identified Small Molecule Inhibitors of Endo-β-N-acetylglucosaminidase (ENGase) for the Treatment of Rare NGLY1 Genetic Disease. Bioorg Med Chem Lett. 2017, 27(13), 2962-2966.

4. High-throughput virtual screening identifies novel N'-(1-phenylethylamine)-benzo hydrazides as potent, specific, and reversible LSD1 inhibitors. J Med Chem 2013, 56 (23), 9496-508.