CH @chem_ml
This post (sticker, poll or similar) has no web preview. Open in Telegram
Forwarded from OdanChem research
Accurate predictive uncertainty quantification (pUQ) is critical for assessing chemical property predictions. Incorrect quantification could lead to costly synthesis and characterization of drug candidates destined to fail, or result in misguided environmental regulations.
This work integrates Conformal Quantile Regression (CQR) with graph neural networks to achieve accurate molecular property prediction and well-calibrated predictive uncertainty quantification. By combining conformal prediction with quantile regression, CQR provides model-agnostic, distribution-free prediction intervals with valid coverage for finite samples. We benchmark it against established pUQ approaches, including ensembling, Monte Carlo dropout sampling, mean-variance estimation, and evidential regression.
Our experiments on datasets spanning quantum mechanics, physical chemistry, and pharmacokinetics show that CQR generally provides strong calibration while maintaining competitive predictive accuracy and uncertainty-ranking performance. It also effectively captures heteroskedastic noise across the data, which is a key requirement for robust pUQ. Furthermore, we demonstrate its efficacy with an uncertainty-guided retrospective virtual screening experiment for antibiotic discovery, achieving hit rates above 90%.
Here, to address this, we present an integrated closed-loop workflow that combines efficient reaction data acquisition with high-accuracy deep learning. We developed an active learning strategy, driven by a tree-based ensemble, to guide the acquisition of the most informative laboratory experiments. This experimental campaign yielded a diverse benchmark for C–H borylation. The bespoke dataset allowed us to train and evaluate geometric graph neural networks.
We demonstrate that augmenting these symmetry-aware models with self-supervised auxiliary tasks consistently improves performance for both reaction outcome prediction and regioselectivity prediction. Prospective tests on unseen substrates, featuring challenging N-heteroaryl motifs, pinpointed the correct borylation positions in all cases, illustrating the practical utility of our workflow for efficient drug optimization.
Forwarded from ATHANOR
We developed MAGI, an open modular agent that authors objectives, launches and monitors optimization, interprets structure-activity relationships, and revises its strategy accordingly. MAGI generates molecules either directly through the LLM or by delegating to REINVENT 4, with scoring services interchangeable behind a common contract. We tested it across nine retrospective lead-optimization campaigns from three pharmaceutical companies, replayed under fixed temporal cutoffs. Both routes produced valid structures: LLM proposals stayed closer to local chemistry and reached comparable or higher primary activity in fewer operations, whereas REINVENT explored broader chemical space.
Whether a campaign met its objective depended on the predictive models, not on the generation route: attainment followed model accuracy on the chemistry proposed, dropping once that chemistry moved outside the model's applicability domain.
Separately, a blinded evaluation asked whether the MAGI's output could pass as expert work: chemists were not able to discriminate agentic proposals from held-out compounds, and judged the SAR reasoning broadly plausible yet incomplete. Together, these results position MAGI as a coordination layer pluggable into existing computational chemistry workflows. The ceiling on real projects, however, remains currently set by scorer applicability rather than by tool orchestration.
This Perspective provides an account of recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model life cycle management.
Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints.
These approaches not only improve robustness and reliability but also enable meaningful human–AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them.
This work introduces ElemeNet, a unified, general-purpose software package for molecular machine learning. The ElemeNet software package enables the training of advanced ML models for diverse properties and data sets with an enlarged range of elemental compositions.
We define molecular representations compatible with elements 1–100, supporting diverse organometallic and biological systems in addition to organic chemistry already well served by the Chemprop ML toolkit. As well as more common atom-, bond-, and molecule-level predictions, we introduce and allow for moiety-level predictions.
We also natively define optional conditioning on charge and spin states. Advanced E(3)-equivariant and transformer architectures are supported in addition to 2D models, with all classes including built-in uncertainty quantification through deterministic and statistical measures. We benchmark our protocols for ML model training against representative data sets from organic, inorganic, coordination, and biological chemistry, achieving competitive and SOTA performance relative to literature baselines and favorable scaling to millions of molecules.
This paper proposes a set of guidelines to incentivize rigorous and domain-appropriate techniques for method comparison tailored to small molecule property modeling. These guidelines, accompanied by annotated examples using open-source software tools, lay a foundation for robust ML benchmarking and thus the development of more impactful methods.
We are delighted to inform you that your submission, "SC³: A Multi-Solvent Solubility Challenge and Benchmark", has been accepted at NeurIPS 2026 ED Track. Congratulations!
There were 3,757 valid paper submissions with a PDF to the NeurIPS ED Track this year, of which the program committee accepted 971 (25.8%) papers in total.
Designing new medicines requires balancing multiple competing objectives, including biological activity, pharmacokinetics and safety. As generative chemistry expands our ability to explore chemical space, the choice of how objectives are defined, combined and evaluated becomes increasingly important. In this Perspective, we discuss the role of multi-objective optimization in molecular generation, highlighting practical considerations, methodological challenges and emerging opportunities for translating these approaches into effective drug discovery tools.
In this Review, we map and quantify key elements of these changes over the past 25 years, such as trends in the number and type of targets through which drugs mediate their therapeutic effects, which now include 686 biomolecules modulated by 1,702 drugs.
Chem ML/AI/Datasets Наконец-то вышла официальная статья по CheMeleon! Deep Learning Foundation Models for Low-Data Regimes from Classical Molecular Descriptors https://doi.org/10.1021/acs.jcim.6c01546 We propose pretraining on low-noise, calculable molecular descriptors via…
We introduce CheMeleon-Rxn, a pre-trained reaction graph neural network that adapts the CheMeleon framework to condensed graphs of reaction (CGR) and demonstrates that descriptor-regression is an effective pre-training strategy for low-data reaction property prediction.
An O(10M)-parameter D-MPNN encoder was pre-trained on #2 million reactions by regressing onto dense reaction descriptors pooled from classical molecular descriptors, then fine-tuned on small reaction property prediction datasets. Across regression tasks spanning gasphase activation energies, reaction enthalpies, experimental yields, and rate coefficients, CheMeleon-Rxn is best or statistically tied-for-best on seven of eight tasks. It outperforms the same encoder trained from scratch, as well as classical-descriptor, fingerprint, and pre-trained-fingerprint baselines.
We further tested the pre-training strategy across various graph neural network architectures and found that its benefit holds for other edge-aware backbones such as GINE, and that it is robust to the choice of descriptor set, pooling operation, and other pre-training hyperparameters. We visualized and analyzed the CheMeleon-Rxn embeddings, which reveal chemically coherent structure in the learned reaction representation space
The model weights will be released upon publication.
Here we show how training language models through Bayesian objectives enables their use as reliable optimizers guided by natural language. Our approach, GOLLuM (Gaussian process Optimized LLMs), teaches LLMs from experimental outcomes under uncertainty, transforming their overconfidence from a fundamental flaw into a precise learning signal. This signal reshapes the LLM embeddings so that experiments with similar outcomes cluster together, revealing structure in the design space.
Starting from only ten low-performing experiments, GOLLuM generalizes across 23 tasks in organic synthesis, materials science, process chemistry and molecular design, ranking first on average among all competing methods.
It matches traditional Bayesian optimization with over 40% fewer experiments and nearly doubles the discovery of high-performing Buchwald–Hartwig reactions over expert quantum-chemical descriptors and state-of-the-art LLMs (43% versus 24–25%). More broadly, GOLLuM points to a different paradigm for specializing foundation models: not through more data but through richer, uncertainty-guided information.
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.
