Integrating digital chemistry within the broader chemistry community

Continued improvements in computer hardware and algorithms have allowed digital chemistry to be employed for studying more complex problems. However, performing the various computational protocols required for high-throughput screening, molecular optimization, and machine learning (ML) model training can be tedious and time-consuming. Automating these workflows minimizes errors, enhances reproducibility, and facilitates storing and reusing the associated data.

In this talk, we present AQME, an automated end-to-end workflow software that performs multi-step tasks of computational chemistry, and ROBERT, a program that automates ML-related tasks such as data curation, hyperparameter optimization, and ML predictor generation. The combination of these programs allows for high-quality reaction energy profile studies and ML predictions to be generated with just a few command lines, starting from simple inputs such as databases with SMILES strings.