# Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory

> **NIH NIH U18** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $562,489

## Abstract

PROJECT SUMMARY
Access to complex chemical matter (e.g., small molecule drug candidates) is a core requirement
for testing biological hypotheses and probing human health. Current approaches to chemical
synthesis rely on time-consuming planning and labor-intensive manual synthesis, which is a
rate-limiting step in the discovery of new functional molecules. This collaborative project
comprises the development of several virtual modules to support the multi-step chemical
synthesis of new molecules in autonomous laboratories. These modules are designed to
benefit traditional synthetic chemists in addition to automation chemists using the integrated
hardware platform being developed by the ASPIRE team at NCATS. Computer-aided synthesis
planning can be viewed as a hierarchical process of elaboration starting from the list of
molecules of interest: (1) retrosynthetic planning to identify suitable starting materials and
intermediates, (2) reaction condition recommendation to identify the conditions with which each
reaction step should be run, (3) translation of hypothetical reaction steps into action sequences
executable on automated hardware. Optional but valuable components include (4) recording
procedures through an experimental planning module, (5) optimization of the timing and order
of action sequences to most efficiently synthesize multiple synthetic targets via a digital twin of
the platform, and (6) the iterative optimization of process parameters based on experimental
responses in a feedback loop. This program will address each of these needs through the
development of new software solutions employing state of the art algorithms in graph network
theory, cheminformatics, deep learning for chemistry, and optimization. Software modules will
be written using established software development best practices for ease of cross-platform
deployment (via containerization) and long-term maintainability (via extensive
documentation). Further, each module will be deployed as an independent microservice with
a common application programming interface (API) format for inter-module communication and
integration with existing NCATS modules, including graphical user interfaces. These efforts will
be accomplished through close partnership between MIT and NCATS to enhance the overall
capabilities of the NCATS ASPIRE platform.

## Key facts

- **NIH application ID:** 10448106
- **Project number:** 1U18TR004149-01
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Connor Wilson Coley
- **Activity code:** U18 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $562,489
- **Award type:** 1
- **Project period:** 2022-06-10 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448106

## Citation

> US National Institutes of Health, RePORTER application 10448106, Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory (1U18TR004149-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10448106. Licensed CC0.

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