Jul 2026· Chemistry· pp.
e71396
· 0 citations· 31 references
Medicine
TL;DR
It is argued that this approach is best understood as compression: deliberate, lossy, and interpretable reductions of chemical details that generalize surprisingly well across molecules, reactions, and domains.
Abstract
Chemistry navigates an immense molecular space using compact, transferable knowledge structures that are neither strict physical laws nor merely qualitative intuition. We argue that this approach is best understood as compression: deliberate, lossy, and interpretable reductions of chemical details that generalize surprisingly well across molecules, reactions, and domains. Machine learning (ML) performs a different kind of compression, optimizing against training objectives rather than chemical judgment. The result is models that excel within their training domains but can fail outside them in ways that are difficult to diagnose, often without providing the mechanistic insight that makes chemical knowledge valuable and transferable. This difference defines an epistemic gap that is the central challenge for artificial intelligence (AI) in chemistry. Physics-based AI systems offer a partial path forward, but we believe this gap will persist for the foreseeable future with important consequences for how chemistry is practiced and taught.
Modern chemistry is pushing the limits of traditional Artificial Intelligence (AI) models, placing unprecedented demands on data availability to address humanity's most pressing challenges. One particular concern is AI's dependence on large, curated data and its tendency to deviate from or misrepresent fundamental chemistry principles. Nonetheless, this concern is often overshadowed by the urgent demand for emergent solutions to real‐world problems. This perspective describes the incorporation of a domain‐specific knowledge representation & reasoning (KR&R) framework with machine learning (ML) for predictive chemistry. KR&R is presented as a framework to represent chemical knowledge, making a formal connection between inductive hypothesis generation and deductive reasoning. By integrating scientific rules into data‐driven processes, upholding a “chemist in the loop” approach, KR&R ensures that ML models are understandable and consistent with existing chemical theory. These concepts are illustrated by case studies where KR&R improves the interpretability of ML predictive models targeting thermodynamic properties (Δ
G
sol
, Δ
vap
H
m
°), reaction yields, and catalytic performance. These examples also show KR&R's importance in managing the complexity of modern computational chemistry, establishing it as a key component of explainable AI in the field.
José Ferraz-Caetano, Filipe Teixeira, M. N. D. S. Cordeiro· WIREs Computational Molecula...· 0 citations
Despite the potential of Large Language Models (LLMs) in chemical discovery, current LLMs still lack fundamental chemical domain knowledge, produce incoherent reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists. To build advanced reasoning capabilities of Chem-R, we design a three-phase training framework, including: 1) Chemical Foundation Training (CFT), which establishes core chemical knowledge. 2) Chemical Reasoning Protocol (CRP) Distillation, incorporating structured, expert-like reasoning traces to guide systematic and reliable problem solving. 3) Chemical Multi-Task Optimization (CMO) that optimizes the model for generalizable capabilities across diverse molecular- and reaction-level tasks. This structured pipeline enables Chem-R to achieve state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5, by up to 19% on molecular tasks and 40% on reaction tasks. Meanwhile, Chem-R also consistently outperforms existing chemical foundation models across both molecular and reaction level tasks. These results demonstrate Chem-R's superior generalization, interpretability, and potential as a foundation for next-generation AI-driven chemical discovery. The code and model are available at https://github.com/davidweidawang/Chem-R.
Weida Wang, Benteng Chen, Di Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
CRISP is introduced, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an executable scalar descriptor supplied to a conventional learner.
Jaehwan Choi, Kunik Jang, Seongmin Kim et al.· 0 citations
In the context of artificial intelligence in chemistry, the first applications that come to mind are widely discussed ones, such as building surrogate models to replace costly numerical simulations or designing new compounds. However, one area where generative AI truly stands out is programming assistance, ranging from code improvement suggestions to vibe coding simple programs from scratch. This article explores how these tools can be used by chemists to test new ideas, make the most of existing open-source software and rapidly develop custom solutions that significantly ease day-to-day work. Using the problem of estimating Hansen solubility parameters as an academic example, their strengths and limitations are illustrated through fully customizable sample codes, including a solubility parameter calculator, a molecular spreadsheet library and a molecular editor. In addition to serving as models, these codes could be of practical interest to some readers due to unique features making them address gaps in the current offerings.
The growing integration of artificial intelligence (AI) and machine learning (ML) is transforming experimental chemistry laboratories. Especially in synthetic chemistry, researchers routinely handle complex and high‐dimensional data, fostering meaningful synergies between chemistry and data science. This review is intended as a practical overview that connects the everyday challenges of synthetic chemists with the digital tools available to address them. It does not seek to explain theoretical foundations of ML or to provide a comprehensive survey of all recent studies in the field. Rather, our goal is to highlight emerging technologies, discuss key considerations for their application, and present a selection of illustrative examples. To begin, we outline the prerequisites for successfully applying data science in synthetic chemistry. Next, we give a realistic overview of strategies and bottlenecks in predictive modeling of molecular properties, reaction outcomes and reaction conditions. We further highlight data‐driven approaches that can be applied in the development of new chemical reactions and synthetic methodologies, including all relevant stages from reaction discovery and optimization to substrate scope evaluation and mechanistic analyses. Finally, we briefly discuss the transformative role of large language models and agentic workflows in synthetic chemistry, focusing on opportunities and challenges in the laboratories of the future.
Niklas Hölter, Felix Katzenburg, Florian Boser et al.· Angewandte Chemie Novit· 0 citations
Balancing chemical equations for organic compounds is a routine yet often tedious component of chemistry education and computational chemistry, and it becomes markedly harder as molecular size and functional-group complexity grow. This paper reviews how artificial intelligence (AI) techniques, ranging from classical algebraic and matrix-based solvers to modern machine-learning models such as graph neural networks and sequence-to-sequence transformers, are being used to formulate, balance, and validate chemical equations for organic reactions. We synthesise findings from the computational chemistry, cheminformatics, and chemistry-education literature to compare rule-based, algebraic, and learned approaches, and we illustrate the underlying logic with a worked example of ethanol combustion solved through the matrix null-space method. The review indicates that deterministic algebraic methods remain the most reliable choice for routine stoichiometric balancing, while learned models add clear value for predicting reaction outcomes and retrosynthetic routes in more complex organic transformations, albeit with lower top-1 accuracy and weaker interpretability. We further discuss the growing use of generative AI tools in chemistry classrooms, including in low-resource settings such as Afghan universities, and conclude that a hybrid architecture combining deterministic solvers with learned chemical-plausibility models is the most promising direction for AI-assisted equation balancing
