Overview

MolMIM is a state-of-the-art generative model for small molecule drug development that learns an informative and clustered latent space. It is a probabilistic auto-encoder that provides a fixed-length representation of variable-length SMILES strings. MolMIM is trained with Mutual Information Machine (MIM) learning and can sample valid SMILES strings from using perturbations of its clustered latent space.

MolMIM can:

• Learn an informative and meaningfully clustered latent space

• Sample valid molecules from from this latent space using an initial seed molecule

• Generate novel small molecules with desired properties under specific constraints

MolMIM’s training procedure promotes a dense latent space which simplifies the process of sampling valid SMILES string. Comparisons to competing techniques demonstrate its superior molecular generation capabilities as measured by validity, uniqueness, and novelty of sampled SMILES strings.

Embedding

Retrieve the embeddings from MolMIM for a given input molecule, allowing for:

• Molecular representation in a high-dimensional space

• Similarity analysis and clustering of molecules

• Use as input for other AI models or algorithms

Hidden State

Retrieve the hidden state from MolMIM for a given input molecule, also known as the “latent code”, allowing for:

• Analysis of the molecular structure’s underlying properties and patterns

• Manipulation and modification of the molecular structure

• Use as input for other AI models or algorithms

Decode

Decode a hidden state representation into a SMILES string sequence, allowing for:

• Generation of novel molecules from a given latent code

• Reconstruction of the original input molecule from its hidden state

• Use as a tool for understanding the relationship between molecular structure and latent space

Sample

Sample the latent space within a given scaled radius from a seed molecule, generating new molecule samples in an unguided fashion, allowing for:

• Exploration of the molecular space around a given molecule

• Use for generating diverse molecule sets or libraries

• Use as a starting point for guided sampling or optimization

Generate

Generate novel molecules (optionally while optimizing against a certain property) using CMA-ES-guided sampling, allowing for:

• Optimization of molecules against specific properties or criteria

• Use for generating molecules with desired properties or characteristics

• Use for improving the quality or performance of generated molecules

NIMs offer a simple and easy-to-deploy route for self-hosted AI applications. Two major advantages that NIMs offer for system administrators and developers are:

1. Increased productivity: NIMs allow developers to build generative AI applications quickly, in minutes rather than weeks, by providing a standardized way to add AI capabilities to their applications.

2. Simplified deployment: NIMs provide containers that can be easily deployed on various platforms, including clouds, data centers, or workstations, making it convenient for developers to test and deploy their applications.

In the context of small molecule drug development, these advantages can:

1. Accelerate lead optimization: NIMs can be used to accelerate the lead optimization process by quickly generating and testing multiple molecular structures, allowing researchers to identify potential leads more efficiently.

2. Streamline data analysis: NIMs can be used to analyze large datasets generated during the drug discovery process, such as molecular dynamics simulations or high-throughput screening data, to identify patterns and trends that can inform the development of new drugs.

3. Improve collaboration: NIMs can facilitate collaboration among researchers by providing a standardized platform for sharing and integrating AI models, enabling teams to work together more effectively and efficiently.

4. Enhance predictive modeling: NIMs can be used to develop and deploy predictive models that can accurately predict the properties and behavior of small molecules, such as their binding affinity or toxicity, allowing researchers to make more informed decisions during the drug development process.

MolMIM is one of many NIMs that can applied within the biosciences. NIMs make it easy to chain models together to develop a complete in silico drug discovery pipeline.