arXiv subjects

Tieu-Long Phan

Publications and source records attributed to Tieu-Long Phan.

5 recordsLinked to original sources

arXiv2026

On the realizability of abstract reaction networks with real molecules and reactions

Abstract reaction networks appear not only as models of chemical reactions but also as models of complex systems, with applications in areas such as ecology and epidemiology, and as one of several alternative paradigms for non-standard computation. It is therefore of interest to determine whether an abstract reaction network can be realized by a concrete set of molecules and plausible chemical reaction mechanisms. It is known that a reaction network has a realization in terms of chemical graphs (i.e., Lewis structures) if and only if it is conservative. Here we consider the problem of assigning a set M of known molecules to a set X of abstract entities in a reaction network (X,R) such that each reaction satisfies mass balance and adheres to one of an allowed set of chemical reaction mechanisms. We show that this problem is NP-complete. Nevertheless, it can be solved in practice using a backtrack-and-prune algorithm inspired by the VF2 family of algorithms originally designed for the subgraph isomorphism problem.

q-bio.MN↗

arXiv2026

Lewis-labeled graphs: curly arrows and fishhooks as executable electron transfers

The curly-arrow formalism is the lingua franca of organic reaction mechanisms, but it is not executable. Molecular graphs used in rule-based modeling encode atomic connectivity while omitting lone pairs, radical electrons, and distinct sigma and pi components. Topological matching therefore cannot determine whether a reactive center holds the electrons a step consumes, while matching on derived formal charge can reject centers that do hold them. We introduce the Lewis-labeled graph (LLG), whose atom labels carry lone-pair and radical populations and whose bond labels separate sigma- and pi-bond occupancies. Bond order, formal charge, and valence electron inventory are derived from these fields. Chemical transformations become resource-constrained double-pushout rules, and curly arrows and coupled fishhooks become locus-sorted electron transfers committed atomically from a common pre-state. For a specified event group, we prove that its execution, application of its induced rule, and the corresponding integral occupancy update are equivalent. Every admissible event conserves valence electrons and net formal charge, and bond-centered fishhook coupling follows from integrality rather than drawing convention. Bidirectional replay recovers all 39,732 mapped reference endpoints, while LLG admits 96 fewer forward and 818 fewer inverse unique outcomes than conventional atom-bond rules. Ten radical records require manual annotation corrections. After review, transition construction succeeds for 101,313 of 101,314 records. The remaining case requires an endpoint atom-map correction rather than an arrow edit. Strict replay accepts all 160 reviewed steps and rejects all 1,120 controlled corruptions. These results establish a common executable state space for molecular graphs, reaction rules, and electron-flow annotations, prior to questions of kinetic or thermodynamic feasibility.

physics.chem-ph↗

arXiv2026

ProDock: From multi-target consensus docking into database-backed storage

Protein--ligand docking is widely used in structure-based discovery, but routine studies often fail at the workflow level rather than at the scoring level. Receptor cleaning, ligand preparation, file conversion, box definition, run organization, and downstream parsing are frequently handled by fragmented scripts, which reduces reproducibility, obscures provenance, and complicates comparative analysis across targets, ligands, and docking settings. We present ProDock, an open-source Python toolkit for reproducible protein--ligand docking and postprocessing. ProDock organizes application-oriented docking into four connected layers: receptor and ligand preprocessing, provenance-aware docking execution, postprocessing of poses and interaction fingerprints, and SQLite-backed storage for later querying. The package supports inputs ranging from PDB identifiers and local receptor files to \texttt{SMILES} strings and prepared ligand directories, and integrates receptor preparation, ligand preparation, reference-ligand-based box generation, campaign serialization, batch docking, pose crawling, score extraction, interaction profiling, and database insertion within a consistent project-local workflow. By representing studies as explicit many-to-many campaigns linking multiple receptors, ligands, and docking backends, ProDock converts fragmented engine-specific outputs into structured analytical results that are easier to compare, reuse, and audit. ProDock is implemented in Python and released under an open-source license at https://github.com/Medicine-Artificial-Intelligence/ProDock. Documentation is available at https://prodock.readthedocs.io/en/latest.

q-bio.QM↗

arXiv2026

SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling

We present SynRXN, a unified benchmarking framework and open-data resource for computer-aided synthesis planning (CASP). SynRXN decomposes end-to-end synthesis planning into five task families, covering reaction rebalancing, atom-to-atom mapping, reaction classification, reaction property prediction, and synthesis route design. Curated, provenance-tracked reaction corpora are assembled from heterogeneous public sources into a harmonized representation and packaged as versioned datasets for each task family, with explicit source metadata, licence tags, and machine-readable manifests that record checksums, and row counts. For every task, SynRXN provides transparent splitting functions that generate leakage-aware train, validation, and test partitions, together with standardized evaluation workflows and metric suites tailored to classification, regression, and structured prediction settings. For sensitive benchmarking, we combine public training and validation data with held-out gold-standard test sets, and contamination-prone tasks such as reaction rebalancing and atom-to-atom mapping are distributed only as evaluation sets and are explicitly not intended for model training. Scripted build recipes enable bitwise-reproducible regeneration of all corpora across machines and over time, and the entire resource is released under permissive open licences to support reuse and extension. By removing dataset heterogeneity and packaging transparent, reusable evaluation scaffolding, SynRXN enables fair longitudinal comparison of CASP methods, supports rigorous ablations and stress tests along the full reaction-informatics pipeline, and lowers the barrier for practitioners who seek robust and comparable performance estimates for real-world synthesis planning workloads.

cs.LG↗

arXiv2025

Prime Implicant Explanations for Reaction Feasibility Prediction

Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and propose an algorithm for computing such explanations in small-scale reaction prediction tasks. Preliminary experiments demonstrate that our notion of prime implicant explanations conservatively captures the ground truth explanations. That is, such explanations often contain redundant bonds and atoms but consistently capture the molecular attributes that are essential for predicting reaction feasibility.

cs.LG↗