Work

Tango*: constrained synthesis planning using chemically informed value functions

Year: 2025

Type: article

Abstract: Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work has introduced specialized bidirectional search algorithms to find synthesis pathways that incorporate pre-selected starting materials, tackling a specific formulation of the starting material-constrained problem. In this work, we introduce a simple guided search-Tango*-which allows solving the starting material-constrained synthesis planning problem using an existing unidirectional search algorithm, Retro*. We show that by optimising a single hyperparameter, Tango* outperforms existing methods in terms of efficiency and solve rate. We also highlight the effectiveness of our computed node cost function in steering synthesis pathways.

Source: Digital Discovery

DOI: 10.1039/d5dd00130g

Authors: Daniel Armstrong, Zlatko Jončev, Jeff Guo, Philippe Schwaller

Institutions: École Polytechnique Fédérale de Lausanne, NCCR Catalysis

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, NCCR Catalysis

Awards: G2159126771, Machine Learning-Enhanced Strategic Synthesis Planning, NCCR Catalysis: Sustainable chamical processes through catalyst design