arXiv AI
Aug 18
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
arXiv:2608. 16111v1 Announce Type: cross Abstract: Retrosynthesis is a cornerstone of drug discovery and organic synthesis.
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model
The paper introduces VenusRX-Bench, a unified benchmark that evaluates forward reaction prediction, single‑step retrosynthesis, and EC‑number prediction for enzymatic reactions, integrating data from multiple biochemical databases with standardized curation and leakage‑controlled splits. It also presents VenusRX, a T5‑style sequence‑to‑sequence model that jointly learns these tasks, leveraging two‑stage training on millions of template‑expanded reactions and real biochemical data, with optional EC conditioning and molecule‑library‑constrained decoding to improve complex biomolecule generation. Across benchmark tasks and challenging generalization splits, VenusRX outperforms or matches existing chemical and enzymatic baselines, demonstrating that incorporating EC information and learned reaction representations yields a bidirectional relationship between reaction structure and catalytic function.
Related stories
arXiv Machine Learning
Aug 7
RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction
arXiv:2608. 06259v1 Announce Type: new Abstract: Reaction yield prediction remains challenging because labeled data are scarce and reaction space is both combinatorially large and sparsely populated, limiting the generalization of existing reaction representations.
arXiv AI
Aug 17
Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation
arXiv:2608. 14076v1 Announce Type: cross Abstract: Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations.
arXiv Machine Learning
Aug 27
A General-Purpose Framework for Chemical Reaction Representation with Atomic Correspondence and Flexible Condition Adaptation
The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.
arXiv Machine Learning
Jul 3
Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases
arXiv:2607. 01362v1 Announce Type: cross Abstract: Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding.
arXiv Machine Learning
Aug 20
Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference
Monroe is a new molecular foundation model that improves upon existing models by pre‑training on over 81 million molecules from the PM6 quantum chemistry dataset, enhancing stereochemistry representation, and introducing novel training losses such as conformer denoising and embedding decorrelation. It also incorporates a prior‑data‑fitted model (TabPFN) for downstream in‑context prediction and demonstrates superior performance on Polaris benchmarks and activity cliff tests. Ablation studies show that the PFN‑based downstream approach can upgrade other models, producing state‑of‑the‑art variants MiniMol_PFN and CheMeleon_PFN.
arXiv AI
Sep 16
LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization
arXiv:2604.07669v3 Announce Type: replace-cross Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...
arXiv Machine Learning
Jul 1
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
arXiv Machine Learning
Oct 3
A Large Scale Investigation of Scaling Limits in Chemical Language Models
The paper reports a large-scale, compute-controlled study of Chemical Language Models (CLMs) involving over 30,000 experiments across different molecular representations, tokenizations, model sizes, datasets, and architectures. It finds clear scaling trends in pretraining loss but shows that these improvements do not translate into proportional gains in goal-directed molecular design, with chemical syntax saturating early while semantic properties develop more slowly. The authors release a new suite of models, NovoMolGen, that achieves state-of-the-art results in drug discovery tasks, highlighting a disconnect between representation learning and downstream design and calling for new pretraining paradigms that target chemical semantics.
arXiv Machine Learning
22h ago
Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Design
arXiv:2507.20130v2 Announce Type: replace Abstract: Recent breakthroughs in generative models have shown great promise for drug discovery, yet Structure-Based Drug Design (SBDD) faces two critical ch...
arXiv AI
Aug 20
PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
PGFS++ is a synthesis‑aware reinforcement learning framework that improves molecular properties while ensuring the resulting molecules can be synthesized and remain structurally similar to the input. It builds on PGFS+ by using trainable embedding lookup tables for reaction templates and second reactants, a more effective scoring function, and a refined RL algorithm. Experiments demonstrate that PGFS++ enhances target properties and preserves high output diversity, overcoming the reward‑hacking failure mode seen in earlier versions.
arXiv Machine Learning
Aug 12
Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
arXiv:2602. 13136v2 Announce Type: replace Abstract: Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization.