M-Life is a life sciences company focused on molecule discovery for new drugs, agricultural chemicals, and diagnostic tools. They leverage their expertise to develop novel chemical compounds with applications across pharmaceuticals, agriculture, and diagnostics.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional drug discovery methods often struggle to efficiently identify novel chemical compounds with desired efficacy and safety profiles due to the complexity of biological systems and the vast chemical space. Evaluating ADME (absorption, distribution, metabolism, and excretion) and toxicity serially can be time-consuming and costly, hindering the rapid development of new drugs, agricultural chemicals, and diagnostic tools.
Solution
M-Life employs a proprietary AI-driven platform, integrating both target-based and phenotype-based discovery, to accelerate the identification and design of novel molecules. The platform utilizes data fusion, molecular transcription to energy-density analogs, and algorithmic assessment via its P2L™ (Pattern-to-Lead) platform. M-Life's MiST™ platform aggregates molecular modalities to build a composited representation of attributes, enabling comprehensive virtual assessment of efficacy, ADME, and toxicity. This integrated approach allows for the repurposing of existing drugs, selection of high-value asset molecules, and the design of novel molecules in a directed fashion.
Target Audience
M-Life's primary customers include pharmaceutical companies, agricultural firms, and diagnostic tool developers seeking to accelerate their discovery programs and identify novel chemical entities with improved properties.
Features
- P2L™ (Pattern-to-Lead) platform: Employs advanced pattern recognition to identify homologous and variable portions of QM energy-density patterns for AI determination of molecular-efficacy relationships.
- MiST™ (Molecular in Silico Tomography) platform: Integrates efficacy models with receptor/ligand docking, toxicity, ADME models, drugability, and synthesis considerations to create a composited solution.
- Data fusion: Combines quantitative and non-quantitative data associated with efficacy and adverse effects.
- Molecular transcription: Converts molecular structures into energy-density analogs for algorithmic assessment.
- Virtual assessment: Integrates desirable and undesirable properties into a comprehensive virtual assessment.
- Focus on multi-targeting agents (MTAs) or chimeras, as well as tubulin targeting molecules.