Learning Continuous Solvent Effects from Transient Flow Data: A Graph Neural Network Benchmark on Catechol Rearrangement

• URL: http://arxiv.org/abs/2512.19530v1
• Date: Mon, 22 Dec 2025 16:19:01 GMT
• Title: Learning Continuous Solvent Effects from Transient Flow Data: A Graph Neural Network Benchmark on Catechol Rearrangement
• Abstract summary: This work introduces the textbfCatechol Benchmark, a high- throughput transient flow chemistry dataset.<n>We evaluate various architectures under rigorous leave-one-solvent-out and leave-one-mixture-out protocols to test generalization to unseen chemical environments.<n>We propose a hybrid GNN-based architecture that integrates Graph Attention Networks (GATs) with Differential Reaction Fingerprints (D RFP) and learned mixture-aware encodings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting reaction outcomes across continuous solvent composition ranges remains a critical challenge in organic synthesis and process chemistry. Traditional machine learning approaches often treat solvent identity as a discrete categorical variable, which prevents systematic interpolation and extrapolation across the solvent space. This work introduces the \textbf{Catechol Benchmark}, a high-throughput transient flow chemistry dataset comprising 1,227 experimental yield measurements for the rearrangement of allyl-substituted catechol in 24 pure solvents and their binary mixtures, parameterized by continuous volume fractions ($\% B$). We evaluate various architectures under rigorous leave-one-solvent-out and leave-one-mixture-out protocols to test generalization to unseen chemical environments. Our results demonstrate that classical tabular methods (e.g., Gradient-Boosted Decision Trees) and large language model embeddings (e.g., Qwen-7B) struggle with quantitative precision, yielding Mean Squared Errors (MSE) of 0.099 and 0.129, respectively. In contrast, we propose a hybrid GNN-based architecture that integrates Graph Attention Networks (GATs) with Differential Reaction Fingerprints (DRFP) and learned mixture-aware solvent encodings. This approach achieves an \textbf{MSE of 0.0039} ($\pm$ 0.0003), representing a 60\% error reduction over competitive baselines and a $>25\times$ improvement over tabular ensembles. Ablation studies confirm that explicit molecular graph message-passing and continuous mixture encoding are essential for robust generalization. The complete dataset, evaluation protocols, and reference implementations are released to facilitate data-efficient reaction prediction and continuous solvent representation learning.

Related papers

• Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation [71.74203645336385]
  Impute-EM alternates between imputing missing entries with the current model and refitting a diffusion backbone on completed data.<n>We characterize the update and show that the observed mask-indexed marginals match the targets at the limit.
  arXiv  Detail & Related papers  (2026-09-14T09:39:13Z)
• ToxLens: A Reproducible Graph-Learning Framework for Leakage-Aware, Uncertainty-Calibrated Molecular Toxicity Prediction [0.0]
  ToxLens is a graph-learning framework for 11 toxicity endpoints spanning Ames mutagenicity, acute oral toxicity, hERG inhibition, and Tox21 nuclear-receptor and stress-response assays.<n>The workflow combines conservative chemical curation, sphere-exclusion filtering, a leakage-aware UMAP-HDBSCAN split, parallel graph and global-feature encoders joined by late concatenation.<n>On the leakage-controlled test fold, a five-seed soft-voting ensemble achieved a Matthews correlation coefficient score of 0.44, an area under the receiver operating characteristic curve score of 0.83, and an area under the
  arXiv  Detail & Related papers  (2026-08-31T08:58:51Z)
• Directed Graph Topology Inference via Graph Filter Identification [54.541275287889164]
  We address the problem of inferring a directed network from nodal measurements generated by a graph convolutional filter.<n>A joint graph filter and topology identification algorithm is also proposed, which alternates between the aforementioned steps to offer improved sample complexity.
  arXiv  Detail & Related papers  (2026-06-25T18:25:57Z)
• Chem-PerturBridge: a harmonized compendium of small molecule perturbation transcriptomic effects [4.860649440823431]
  Chem-PerturBridge is a harmonized multi-dataset resource comprising over 37k compounds, 136 cellular contexts, and 1.25M transcriptomic samples.<n>We use the resource to evaluate matched-condition agreement across datasets and replicate agreement within datasets.
  arXiv  Detail & Related papers  (2026-05-29T16:38:30Z)
• A Systematic Evaluation of Molecular Mixture Behavior Prediction [0.0]
  We propose an evaluation framework that decomposes mixture-property error into pure-compound and interaction (non-ideal) components.<n>We show that strong absolute accuracy can mask poor recovery of non-ideal mixture behavior.<n>These results identify transfer to unseen molecules as a central challenge in molecular mixture machine learning.
  arXiv  Detail & Related papers  (2026-05-28T09:58:59Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery [3.4776689202566824]
  We introduce a pre-training method that incorporates ligand conformational ensembles generated under diverse solvent conditions as augmented input.<n>The training process integrates molecular reconstruction to capture local geometry, interatomic distance prediction, and contrastive learning to build solvent-invariant molecular representations.<n>The framework supports solvent-aware, multi-task modeling and produces consistent results across benchmarks.
  arXiv  Detail & Related papers  (2025-08-03T15:25:42Z)
• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [53.24886143129006]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel bilevel optimization approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum [78.27945336558987]
  Decentralized server (DFL) eliminates reliance on client-client architecture.<n>Non-smooth regularization is often incorporated into machine learning tasks.<n>We propose a novel novel DNCFL algorithm to solve these problems.
  arXiv  Detail & Related papers  (2025-04-17T08:32:25Z)
• Robust Graph-Based Semi-Supervised Learning via $p$-Conductances [49.0776396776252]
  We study the problem of semi-supervised learning on graphs in the regime where data labels are scarce or possibly corrupted.<n>We propose an approach called $p$-conductance learning that generalizes the $p$-Laplace and Poisson learning methods.<n> Empirical results on computer vision and citation datasets demonstrate that our approach achieves state-of-the-art accuracy in low label-rate, corrupted-label, and partial-label regimes.
  arXiv  Detail & Related papers  (2025-02-13T01:11:25Z)
• A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility [5.763661159910719]
  Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors.
  arXiv  Detail & Related papers  (2024-09-06T14:20:38Z)
• Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data [0.0]
  Bi-Directional Long Short Term Memory (BiLSTM) is a variant of Recurrent Neural Network (RNN) that processes input molecular sequences. The proposed work aims to understand the sequential patterns encoded in the SMILES strings, which are then utilised for predicting the toxicity of the molecules.
  arXiv  Detail & Related papers  (2024-07-08T18:12:11Z)
• YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention [9.018408514318631]
  Traditional methods often miss complex molecular structures, leading to inaccuracies. We introduce the YZS-Model, a deep learning framework integrating Graph Convolutional Networks (GCN), Transformer architectures, and Long Short-Term Memory (LSTM) networks. YZS-Model achieved an $R2$ of 0.59 and an RMSE of 0.57, outperforming benchmark models.
  arXiv  Detail & Related papers  (2024-06-27T12:40:29Z)
• Graph Out-of-Distribution Generalization with Controllable Data Augmentation [51.17476258673232]
  Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. Due to the selection bias of training and testing data, distribution deviation is widespread. We propose OOD calibration to measure the distribution deviation of virtual samples.
  arXiv  Detail & Related papers  (2023-08-16T13:10:27Z)
• End-to-End Supervised Multilabel Contrastive Learning [38.26579519598804]
  Multilabel representation learning is recognized as a challenging problem that can be associated with either label dependencies between object categories or data-related issues. Recent advances address these challenges from model- and data-centric viewpoints. We propose a new end-to-end training framework -- dubbed KMCL -- to address the shortcomings of both model- and data-centric designs.
  arXiv  Detail & Related papers  (2023-07-08T12:46:57Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Categorizing Semantic Representations for Neural Machine Translation [53.88794787958174]
  We introduce categorization to the source contextualized representations. The main idea is to enhance generalization by reducing sparsity and overfitting. Experiments on a dedicated MT dataset show that our method reduces compositional generalization error rates by 24% error reduction.
  arXiv  Detail & Related papers  (2022-10-13T04:07:08Z)
• Entropy Minimizing Matrix Factorization [102.26446204624885]
  Nonnegative Matrix Factorization (NMF) is a widely-used data analysis technique, and has yielded impressive results in many real-world tasks. In this study, an Entropy Minimizing Matrix Factorization framework (EMMF) is developed to tackle the above problem. Considering that the outliers are usually much less than the normal samples, a new entropy loss function is established for matrix factorization.
  arXiv  Detail & Related papers  (2021-03-24T21:08:43Z)
• Unassisted Noise Reduction of Chemical Reaction Data Sets [59.127921057012564]
  We propose a machine learning-based, unassisted approach to remove chemically wrong entries from data sets. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets.
  arXiv  Detail & Related papers  (2021-02-02T09:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.