The AIDD Code - Model Medicines

A New Standard

The AIDD Code

In the rapidly evolving landscape of AI-driven drug discovery, pharmaceutical companies and investors need a clear framework for assessing the capabilities and potential of AI drug discovery companies. At Model Medicines, we propose a new standard: "The AIDD Code." This set of criteria aims to differentiate the most promising players in the field and provide a comprehensive measure of their ability to revolutionize the drug discovery process utilizing AI.

The AIDD Code

New Biology/Target Discovery

The ability to identify novel drug targets or new biological insights

Demonstrate the capacity to move beyond known targets and explore uncharted regions in the vast landscape of human biology. By leveraging the power of AI and machine learning, the industry can uncover new opportunities for therapeutic intervention and potentially address diseases with high unmet medical needs.

Model Medicines

Our proprietary GALILEO™ platform has enabled us to identify novel targets, such as the RdRp Thumb-1 site.

Discovery of RdRp Thumb-1 as a novel broad-spectrum antiviral family of targets and MDL-001 as a potent broad-spectrum inhibitor thereof - Part I: A Bioinformatics and Deep Learning Approach

New Chemical Entities

The generation of New chemical entities (NCEs)

Design and optimize new molecules that are structurally distinct from existing compounds. These NCEs should possess desirable properties, such as improved potency, selectivity, and drug-like characteristics. By creating entirely new chemical matter, the industry can expand the universe of potential therapeutic agents and overcome the limitations of traditional drug libraries.

Model Medicines

We’ve generated a library of new chemical entities for pan-antiviral activity.

GALILEO Generatively Expands Chemical Space and Achieves One-Shot Identification of a Library of Novel, Specific, Next Generation Broad-Spectrum Antiviral Compounds at High Hit Rates

Zero-Shot Hit Rates above 30% at a concentration under 20μM

Repeatable, high hit rates for drug-like compounds

Demonstrate high hit rates in zero-shot learning experiments, with a minimum of 30% of their AI-generated compounds showing activity at a concentration below 20μM in relevant assays. Zero-shot learning refers to the ability of AI models to make accurate predictions on novel targets or chemical spaces without explicit training data. This capability showcases the generalizability and transferability of the AI platform, enabling the discovery of new therapeutic opportunities beyond the confines of existing knowledge.

Model Medicines

We’ve generated new compounds with an unprecedented 66.7% hit rate at a concentration at 10μM for pan-antiviral activity, including a 46.7% Sub-10 μM IC50 discovery rate.

GALILEO Generatively Expands Chemical Space and Achieves One-Shot Identification of a Library of Novel, Specific, Next Generation Broad-Spectrum Antiviral Compounds at High Hit Rates

Tanimoto Scores below 0.4

Compounds that are significantly dissimilar from their training set

Tanimoto score is a metric that measures the structural similarity between two molecules. The industry should aim to generate compounds with Tanimoto scores below 0.4 when compared to existing drugs or known compounds. A low Tanimoto score indicates that the AI-generated molecules are structurally novel and diverse, exploring new regions of chemical space. This is crucial for identifying new lead compounds that may have improved properties over existing drugs.

Model Medicines

In our recent preprint, we showcase our ability to generate novel, diverse compounds with an average Tanimoto similarity score of just 0.36 to the training set, highlighting our platform's capacity to explore new chemical spaces.

ChemPrint: An AI-Driven Framework for Enhanced Drug Discovery

A Therapeutic Pipeline Agnostic to Biology and Disease

A pipeline demonstrated across multiple areas of biology and disease

A truly powerful AI drug discovery platform should be versatile and applicable across various biological targets and disease areas. Companies should showcase a robust pipeline that spans multiple therapeutic domains, such as oncology, neurology, immunology, and infectious diseases. This demonstrates the scalability and adaptability of their AI platform, and its potential to generate a diverse portfolio of drug candidates. By not limiting themselves to a narrow focus, these companies can maximize their impact on human health.

Model Medicines

With 190+ compounds for 25+ targets, our pipeline spans multiple therapeutic areas, including oncology, infectious diseases, and metabolic disorders.

Pipeline

Preclinical Proof of Concept in a Gold Standard Animal Model or Beyond

The ability to create drugs, not merely hits

The ultimate and most important test of an AI drug discovery company's capabilities is the successful translation of their AI-generated compounds into viable drug candidates. At a minimum, companies should provide evidence of preclinical proof of concept for at least one of their assets in a gold-standard animal model. This validates the biological activity and therapeutic potential of the compounds identified by their AI platform and serves as a critical milestone on the path to clinical development.

Model Medicines

We have successfully validated our lead asset, MDL-001, in preclinical, gold-standard animal models at the Garcia-Sastre Laboratory, part of the Icahn School of Medicine at Mount Sinai.

MDL-001: An Oral, Safe, and Well-Tolerated Broad-Spectrum Inhibitor of Viral Polymerases

We developed AmesNet, a novel deep learning model that outperforms existing state-of-the-art QSAR algorithms, particularly in accurately identifying mutagenic potential of out-of-domain chemical entities

AmesNet: A Deep Learning Model Enhancing Generalization in Ames Mutagenicity Prediction

At Model Medicines, we are proud to embody "The AIDD Code"

We believe that "The AIDD Code" will become the new standard for evaluating AI drug discovery companies, and we invite our peers, partners, and investors to adopt this framework. By adhering to these rigorous criteria, we can accelerate the development of life-changing therapies and unlock AI's full potential in revolutionizing the pharmaceutical industry.

Daniel Haders II, Ph.D.

Co-founder, CEO