is opening new pathways beyond rational design.

Unlike other drug discovery platforms that rely solely on simulations and theoretical models, MolecuLern harnesses the immense power of real empirical data.

• Hundreds of “tech-bio” companies are using AI to replicate the traditional drug development process; not change it. Computing power is being employed to do tests quicker or with improved accuracy. This isn't AI per se; just better use of computing power.
• MolecuLern was trained as a true AI tool, thus removing the need to perform certain work in the traditional development process because the AI engine is only generating molecules with certain characteristics; it has been taught how to do so.
• MolecuLern is being fed proven proprietary data; not consuming data points that have yet to be understood or assigned a value. This includes wet lab data and a high quality compound library. The quality of what MolecuLern is trained on is a key differentiator.
• MolecuLern is leveraging AI to learn how to reduce/eliminate steps and factors that add time, complexity and risk to early stages of drug development.
• MolecuLern is getting better hits on compounds because:
• We trained it to know what to look for.
• We need fewer hits (equates to shorter time) because we don't screen everything. We only screen for known properties of value.
• The more the MolecuLern process runs new searches and is fed resulting pre-clinical test data, the more it learns how to create compounds with stronger drug-like properties.
• For companies looking to reduce development time by up to 90% while increasing the probability of success, MolecuLern is a proven solution.