Navigating the Maize: cyclic and conditional computational graphs for molecular simulation
Artikel i vetenskaplig tidskrift, 2024

Many computational chemistry and molecular simulation workflows can be expressed as graphs. This abstraction is useful to modularize and potentially reuse existing components, as well as provide parallelization and ease reproducibility. Existing tools represent the computation as a directed acyclic graph (DAG), thus allowing efficient execution by parallelization of concurrent branches. These systems can, however, generally not express cyclic and conditional workflows. We therefore developed Maize, a workflow manager for cyclic and conditional graphs based on the principles of flow-based programming. By running each node of the graph concurrently in separate processes and allowing communication at any time through dedicated inter-node channels, arbitrary graph structures can be executed. We demonstrate the effectiveness of the tool on a dynamic active learning task in computational drug design, involving the use of a small molecule generative model and an associated scoring system, and on a reactivity prediction pipeline using quantum-chemistry and semiempirical approaches.

Författare

Thomas Lohr

AstraZeneca AB

Michele Assante

University of Cambridge

AstraZeneca AB

Michael Dodds

AstraZeneca AB

University of St Andrews

Lili Cao

AstraZeneca AB

Mikhail Kabeshov

AstraZeneca AB

Jon-Paul Janet

AstraZeneca AB

Marco Klaehn

AstraZeneca AB

Ola Engkvist

Chalmers, Data- och informationsteknik

Digital Discovery

2635098X (eISSN)

Vol. In Press

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1039/d4dd00288a

Mer information

Senast uppdaterat

2024-11-27