Automatically identify the best molecules and reduce synthesis costs. MIT develops a molecular design decision-making algorithm framework

Editor | Ziluo

The use of AI to streamline drug discovery is exploding. Screen billions of candidate molecules for those that may have properties needed to develop new drugs. There are so many variables to consider, from material prices to the risk of error, that weighing the costs of synthesizing the best candidate molecules is no easy task, even when scientists use AI.

Here, MIT researchers developed SPARROW, a quantitative decision algorithm framework, to automatically identify the best molecular candidates, thereby minimizing synthesis costs while maximizing the likelihood that the candidate has the desired properties. The algorithm also identified the materials and experimental steps required to synthesize these molecules.

SPARROW takes into account the cost of synthesizing a batch of molecules at once, since multiple candidate molecules can often be derived from some of the same compounds. Furthermore, this unified approach enables access to critical information for molecular design, property prediction, and synthesis planning from online repositories and widely used AI tools.

In addition to helping pharmaceutical companies discover new drugs more efficiently, SPARROW can also be used to invent new agricultural chemicals or discover specialized materials for organic electronics.

Relevant research titled "An algorithmic framework for synthetic cost-aware decision making in molecular design" was published on "Nature Computational Science" on June 19.

Paper link: https://www.nature.com/articles/s43588-024-00639-y

"Selection of compounds is an art, and sometimes it is a very successful art. But given that we have all these models and prediction tools that provide information about how molecules might behave and be synthesized, we should use that information to guide the decisions we make," said Connor, corresponding author of the paper and an assistant professor in the Department of Chemical Engineering at MIT. Coley said.

Quantitative decision-making algorithm framework SPARROW

"Synthesis Planning And Rewards-based Route Optimization Workflow, SPARROW" is an algorithmic decision-making framework used to drive the design cycle.

Illustration: Overview of SPARROW and its role in the molecular design cycle. (Source: Paper)

This research builds on earlier problem formulations for the simultaneous selection of synthetic routes for multiple molecules, and the integration of product and process system design. Unlike traditional screening methods, SPARROW uses a multi-objective optimization criterion that balances cost and utility to prioritize molecules and their putative synthetic routes from a library of candidate molecules.

SPARROW generates reaction networks consisting of candidate target molecules and synthetic routes. By solving graph-based optimization problems, a set of molecules and synthetic routes can be screened to optimally balance cumulative synthetic cost and utility. In this context, utility measures the value of assessing a molecular property.

Appropriate measures of utility will vary at different stages of application and design. It may include predictions of molecular properties, uncertainties in these predictions, or the potential of new data points to improve structure-property relationships. A library of candidates must be provided to SPARROW with a corresponding reward indicating the utility associated with each candidate molecule.

Illustration: SPARROW’s problem statement. (Source: Paper)

The reward for choosing a molecule also depends on the success of the chosen reaction steps to synthesize that molecule. If a reaction step in the synthetic route of a candidate molecule fails, no information is gained. The researchers formalized this by maximizing the expected reward of selecting a candidate molecule, which can be expressed as its reward multiplied by the probability of successfully synthesizing the molecule.

Balancing cost and utility, the goal of SPARROW can be formalized as the expected reward of all selected goals divided by the cost of synthesizing all selected goals using the selected route.

Complex cost considerations

In a sense, whether scientists should synthesize and test a certain molecule comes down to a question of the cost of synthesis versus the value of the experiment. However, determining cost or value is a difficult problem in itself.

SPARROW addresses this challenge by taking into account the shared intermediate compounds involved in synthesizing a molecule and incorporating this information into its cost vs. value function.

“When you think about the optimization problem of designing a batch of molecules, the cost of adding new structures depends on the molecules you’ve already chosen,” Coley said.

The framework also takes into account factors such as the cost of the starting materials, the number of reactions involved in each synthesis route, and the likelihood of those reactions being successful on the first try.

To use SPARROW, scientists provide a set of molecular compounds they are considering testing, along with definitions of the properties they hope to find.

Next, SPARROW collects information about the molecules and their synthesis pathways, then weighs the value of each molecule against the cost of synthesizing a batch of candidates. It automatically selects the best subset of candidates that meet user criteria and finds the most cost-effective synthetic routes for these compounds.

Jenna Fromer, the first author of the paper, said: "It does all these optimizations in one step, so it can capture all these competing goals at the same time."

Multi-functional framework

SPARROW is unique in that it can be integrated Molecular structures designed by humans, existing in virtual catalogs, or never-before-seen molecular structures created by generative AI models.

“We have a variety of different sources of ideas. Part of the appeal of SPARROW is that you can put all these ideas on a level playing field,” Coley added.

Researchers demonstrate SPARROW’s ability to orchestrate molecular design cycles through three case studies. These applications illustrate how SPARROW (1) successfully balances information gain with synthesis costs, (2) captures the nonadditivity of synthesis costs for a batch of molecules, and (3) scales to candidate libraries containing hundreds of molecules.

Illustration: Demonstration of SPARROW’s ability to balance costs and rewards across a library of 14 ASCT2 inhibitor candidates. (Source: Paper)

They found that SPARROW effectively captured the marginal cost of batch synthesis and identified common experimental steps and intermediate chemicals. Furthermore, it can be expanded to handle hundreds of potential molecular candidates.

「In the chemical machine learning community, there are many models that work well for retrosynthesis or molecular property prediction, but how do we actually use them? Our framework aims to leverage the value of these preliminary studies. By creating SPARROW, We hope to guide other researchers in thinking about compound screening using their own cost and utility functions," Fromer said.

In the future, researchers hope to incorporate more complexity into SPARROW. For example, they hope to enable algorithms to take into account that the value of testing a compound may not always be constant. They also want to include more parallel chemical elements in their cost versus value functions.

Reference content: https://news.mit.edu/2024/smarter-way-streamline-drug-discovery-0617

The above is the detailed content of Automatically identify the best molecules and reduce synthesis costs. MIT develops a molecular design decision-making algorithm framework. For more information, please follow other related articles on the PHP Chinese website!