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This work addresses the fragmented and labor-intensive pipeline in scientific machine learning—from data acquisition to model deployment—by proposing an end-to-end, high-throughput, and agent-collaborative reproducible workflow platform. The platform enables full automation of data collection, processing, modeling, validation, selection, and reporting through modular components, configuration-driven mechanisms, and standardized artifacts. Key technical features include plug-and-play model architectures, deterministic data splitting, batch execution, and structured outputs. Evaluated on quantum mechanics, physicochemical property, and bioactivity prediction tasks, the system achieves state-of-the-art performance and successfully generalizes to non-molecular domains such as time series, significantly enhancing research efficiency and reproducibility.

本文提出ChemXRG框架，通过生成可执行程序解决3D化学反应动画的可视化问题，利用0.8B模型生成领域特定语言并验证转换。

This work aims to achieve computable chemical synthesis—i.e., precise, code-driven control of reaction pathways on general-purpose reconfigurable hardware to automate the synthesis of any stable, isolable molecule while satisfying mass conservation, finite reaction time, and analytical detectability constraints. Method: We introduce the “chemputation” paradigm, modeling synthesis as graph transformations over the space Reagents × Process × Catalyst. We formally define and prove the Universal Chemical Synthesis Theorem, introduce the notion of “analytically reachable quantity” for molecules, and establish dynamic error correction as essential. Our end-to-end implementation integrates the Chemputer hardware platform, the chempiler compiler, assembly-theory–driven reachability analysis, and a real-time sensing feedback framework. Results: Experimental validation demonstrates that chemical reactions are intrinsically programmable, observable, and correctable graph operations. We identify reactor count and sensor bandwidth as critical scalability bottlenecks for chemputation.

This work addresses the synthetic accessibility bottleneck in molecular discovery by proposing a syntax–semantics decoupled two-level program synthesis framework. At the syntax level, Markov Chain Monte Carlo (MCMC) searches over molecular skeleton grammars; at the semantics level, a policy network—trained on fixed skeletons—generates executable retrosynthetic reaction pathways. For the first time, molecular synthesis is formulated as a structured program synthesis problem, enabling user-specified resource constraints (e.g., step count, available reagents) and inherently favoring concise, high-feasibility routes. The method achieves state-of-the-art performance on synthesizable drug-like molecule generation and analogy-based optimization of non-synthesizable molecules. It provides explicit, interpretable synthesis pathways, supports automatic pathway simplification, and integrates seamlessly with autonomous synthesis platforms. This framework establishes a novel paradigm for AI-driven retrosynthetic planning, bridging symbolic reasoning with deep learning while ensuring chemical validity and practical deployability.

To address the critical bottleneck in drug discovery—where molecular generation models neglect synthetic feasibility, hindering experimental validation—this work proposes a novel molecular generation framework projectable onto synthetically accessible chemical space. Methodologically, it introduces synthesis path expressions (SPEs) as a novel molecular representation that intrinsically encodes retrosynthetic logic, and designs a graph-based Transformer architecture for end-to-end translation from molecular graphs to SPEs. This formulation inherently guarantees synthetic feasibility of generated molecules and enables structure-preserving, synthetically constrained analog generation for initially infeasible candidates. Experiments demonstrate substantial improvements in retrosynthetic planning accuracy and successful re-mapping of multiple state-of-the-art generative model outputs—previously deemed synthetically intractable—into property-preserved, experimentally viable analogs. The approach effectively bridges the gap between de novo molecular generation and practical synthesis.

Existing benchmarks for chemical reasoning evaluation focus solely on final answers, making it difficult to detect logical errors in intermediate reasoning steps. To address this limitation, this work proposes ChemCoTBench-V2, a novel benchmark that employs expert-designed structured templates to guide models in generating verifiable intermediate reasoning states. By integrating deterministic chemical rules, reference trajectory alignment, and oracle-verifiable state constraints, the framework enables low-cost, auditable process-level evaluation without requiring human or LLM-based adjudication. This approach is the first to support state-constraint verification and precise error localization in open-ended tasks, revealing a significant discrepancy between answer correctness and reasoning consistency across mainstream large language models. It further facilitates fine-grained model comparison and identification of the first erroneous step in reasoning trajectories.

This work addresses the lack of systematic evaluation for symbolic, verifiable reasoning over molecular graph structures in current chemical large language models. Existing benchmarks often suffer from label bias or information leakage, hindering precise diagnosis of model shortcomings. To bridge this gap, we propose MolecularIQ—the first evaluation framework specifically designed for symbolic reasoning on molecular graphs. By integrating molecular graph representations, symbolic logic verification, and carefully structured reasoning tasks, MolecularIQ establishes a fine-grained benchmark that effectively uncovers systematic failure modes of contemporary models across specific molecular structures and reasoning challenges. This framework provides interpretable diagnostic insights and actionable directions for developing chemical large language models with faithful structural understanding capabilities.

Traditional retrosynthetic tools are constrained by reaction databases and struggle to devise creative synthetic routes for highly functionalized, polycyclic natural products. This work proposes SynthEx, a framework that leverages large language models to construct an intelligent agent system employing a strategy-first planning mechanism. By generating competitive synthetic strategies, integrating critical and routine steps, and incorporating self-reflection for iterative refinement, SynthEx achieves high-quality retrosynthetic planning. Notably, it produces key disconnections comparable to those devised by human experts—validated as authentic and feasible by chemists in blind evaluations. The method successfully designs highly convergent routes for over a thousand natural products and introduces SynthAtlas, an open-access database of these pathways, which has garnered recognition from domain experts.

This study addresses the unclear performance bottlenecks of AI agents in drug discovery by proposing MAGI, a modular agent designed to investigate whether tool orchestration or predictive model accuracy constrains practical outcomes. Methodologically, MAGI employs an open modular architecture coordinating molecular design, optimization monitoring, and SAR analysis with self-revising strategies. It integrates a dual-pathway mechanism combining direct large language model generation with REINFORT-delegated generation, alongside a pluggable scoring service contract. Validation on retrospective pharmaceutical projects reveals that the primary bottleneck lies in the applicability domain of scoring models rather than agent orchestration capabilities. Furthermore, MAGI generates molecules approaching expert-level quality and integrates effectively into existing computational chemistry workflows, thereby clarifying the practical role of AI agents in real-world drug discovery scenarios.

This work addresses the heavy reliance on manual effort in chemical process modeling, which is prone to catastrophic failure due to single-point errors. To overcome this limitation, the authors propose a role-adaptive collaborative framework that decomposes the modeling task into seven specialized sub-roles. By integrating natural language, process flow diagrams, and domain knowledge, the framework generates structured models through typed intermediate representations and a deterministic engineering gating mechanism, enabling automated optimization. Leveraging a fine-tuned Qwen large language model for three critical roles—visual, topological, and specification—the system is integrated with a LangGraph workflow and the IDAES/Pyomo solvers. Evaluated on 82 held-out cases from the OpenIDAES-450 dataset, the approach achieves a 91.5% model construction success rate, with F1 scores of 0.815, 0.791, and 0.782 for unit operations, material streams, and connections, respectively.