Date: February 17, 2026 Commit: fcc0896 Repository: maranasgroup/MechFind Initial Release: MechFind v1.0 We are proud to introduce MechFind, a computational framework for the de novo prediction of detailed enzyme reaction mechanisms. MechFind bridges the "mechanism gap" in bioinformatics by generating elementally and charge-balanced mechanistic hypotheses using only overall reaction stoichiometry as input. This release accompanies our publication: "MechFind: A computational framework for de novo prediction of enzyme mechanisms" (Hartley et al., 2026). Key Features Stoichiometry-Only Input: Predicts mechanisms without requiring 3D protein structures or user-supplied active site residues. Moiety-Based Abstraction: Uses a novel graph-based encoding where reaction steps are modeled as the gain or loss of specific chemical moieties (defined by canonical SMILES). Hybrid Optimization Strategy: Parsimony: A Mixed-Integer Linear Programming (MILP) formulation (minRules) identifies the fewest number of steps required to balance the reaction. Ordering: A secondary formulation (OrderRules) determines a chemically feasible sequence for those steps. Similarity Re-Ranking: Scans candidate mechanisms against the Mechanism and Catalytic Site Atlas (M-CSA) to re-rank predictions based on their resemblance to known, validated biological chemistry. High-Throughput Capability: Capable of processing large databases; benchmarked on 14,000+ reactions from the Rhea database. Included Data Curated Rule Set: Includes Unique_Rules.csv, a matrix of 4,091 elementary reaction rules derived from 734 manually curated M-CSA mechanisms. Arrow Environments: Includes M-CSA_arrow_rules_r0.json, containing the electronic arrow-pushing environments used for the similarity scoring algorithm. Validation Datasets: Pre-processed reaction SMILES for benchmarking against M-CSA and Rhea entries. Installation & Dependencies MechFind is written in Python 3.8+ and runs via Jupyter Notebooks for easy interaction. Dependencies: rdkit (Cheminformatics backend) pulp (Linear programming interface) pandas, numpy (Data manipulation) Quick Start: git clone https://github.com/maranasgroup/MechFind.git cd MechFind pip install -r requirements.txt jupyter notebook MechFind_example.ipynb Usage The release includes a demo notebook (MechFind_example.ipynb) that walks users through: Loading the elementary rule database. Defining a target reaction (substrate/product SMILES). Running the MechFind prediction pipeline. Visualizing the predicted mechanisms as step-by-step moiety change matrices. License This project is licensed for non-profit/non-commercial use only. See the LICENSE file for details regarding commercial licensing via Penn State University.