Accurate prediction of thermodynamic solubility by machine learning remains a challenge.

We investigated over 20 years of published solubility datasets and models, highlighting overlooked datasets and the overlaps between popular sets.

We benchmarked recently published models on a novel curated solubility dataset and report poor performances. We also propose a workflow to cure aqueous solubility data aiming at producing useful models for bench chemist.

Our results demonstrate that some state-of-the-art models are not ready for public usage because they lack a well-defined applicability domain and overlook historical data sources.

We report the impact of factors influencing the utility of the models: interlaboratory standard deviation, ionic state of the solute and data sources. The herein obtained models, and quality-assessed datasets are publicly available.

In this paper, we report a crystal structure prediction (CSP) method with state of the art accuracy and efficiency, validated on a large and diverse dataset including 66 molecules with 137 experimentally known polymorphic forms. The method combines a novel systematic crystal packing search algorithm and the use of machine learning force fields in a hierarchical crystal energy ranking.

Our method not only reproduces all the experimentally known polymorphs, but also suggests new low energy polymorphs yet to be discovered by experiment that might pose potential risks to development of the currently known forms of these compounds.

In addition, we report the prediction results of a blinded study, results for Target XXXI from the seventh CSP blind test, and demonstrate how the method can be used to accelerate clinical formulation design and derisk downstream processing.

In this work, we introduce a new open-source Raman dataset consisting of pure chemical compounds commonly employed in the development of APIs. By curating and publishing this dataset, we aim to provide the scientific community with access to high-quality, reusable data.

Containing 3,510 samples spanning 32 compounds, this data can be utilised for referencing and can potentially facilitate in the development of more accurate and generalisable calibration models when access to reference data is limited.

Here we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pretraining and fine-tuning paradigm. To support the evaluation of nuclear magnetic resonance chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve competitive performance in both liquid-state and solid-state nuclear magnetic resonance datasets, demonstrating its robustness and practical utility in real-world scenarios.

We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability.

Our approach uses Positive and Unlabeled (PU) learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets.

By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency.

In this Outlook, we delve into various prompt engineering techniques and illustrate relevant examples for extensive research from metal–organic frameworks and fast-charging batteries to autonomous experiments.

We also elucidate the current limitations of prompt engineering with LLMs such as incomplete or biased outcomes and constraints imposed by closed-source limitations.

Although LLM-assisted chemical research is still in its early stages, the application of prompt engineering will significantly enhance accuracy and reliability, thereby accelerating chemical research.

In this review, we aim to distill insights from current research on employing transformer models for Molecular Property Prediction (MPP). We analyze the currently available models and explore key questions that arise when training and fine-tuning a transformer model for MPP. These questions encompass the choice and scale of the pretraining data, optimal architecture selections, and promising pretraining objectives.

Our analysis highlights areas not yet covered in current research, inviting further exploration to enhance the field’s understanding. Additionally, we address the challenges in comparing different models, emphasizing the need for standardized data splitting and robust statistical analysis.

This study investigates the influence of using multiple conformers in machine learning-based property prediction, comparing two- and three-dimensional descriptors using three independent data sets: a large-scale quantum mechanical property, a medium-scale melting point, and small-scale enantioselective chemical reaction data sets.

One unique aspect of this study is creating these carefully controlled data sets for models’ performance evaluation in conformational diversity and the target property’s dependence on conformation.

Our findings show that using all available conformers as simple data augmentation consistently achieves high prediction accuracy among aggregation approaches, followed by mean aggregation.

Herein, we apply a domain-adaptation-based transfer-learning (TL) approach to photocatalysis. Despite being different reaction types, the knowledge of the catalytic behavior of organic photosensitizers (OPSs) from photocatalytic cross-coupling reactions is successfully transferred to ML for a [2+2] cycloaddition reaction, improving the prediction of the photocatalytic activity compared with conventional ML approaches. Furthermore, a satisfactory predictive performance is achieved by using only ten training data points. 

The review will cover the development, promise, and limitations of “traditional” computational chemistry as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure–property relationships in transition-metal chemistry.

We aim to highlight how unique considerations in motifs of metal–organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Foundation models are an emerging paradigm in artificial intelligence (AI), with successful examples like ChatGPT transforming daily workflows. Generally, foundation models are large-scale, pretrained models capable of adapting to various downstream tasks by leveraging extensive data and model scaling.

Their success has inspired researchers to develop foundation models for a wide range of chemical challenges, from materials discovery to understanding structure–property relationships, areas where conventional machine learning (ML) models often face limitations.

In addition, foundation models hold promise for addressing persistent ML challenges in chemistry, such as data scarcity and poor generalization. In this perspective, we review recent progress in the development of foundation models in chemistry across applications of varying scope.

This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. These equations demonstrate satisfactory accuracy and generalizability, providing a scientific basis for the selection of the experimental conditions, and contributing to a better understanding of chromatography. 

Here, we introduce a lightweight computational strategy for antimicrobial discovery that builds on MolE (Molecular representation through redundancy reduced Embedding), a self-supervised deep learning framework that leverages unlabeled chemical structures to learn task-independent molecular representations.

By combining MolE representation learning with available, experimentally validated compound-bacteria activity data, we design a general predictive model that enables assessing compounds with respect to their antimicrobial potential.

Our model correctly identifies recent growth-inhibitory compounds that are structurally distinct from current antibiotics. Using this approach, we discover de novo, and experimentally confirm, three human-targeted drugs as growth inhibitors of Staphylococcus aureus.

In this study, we introduce the QDπ dataset which incorporates data taken from several datasets. We use a query—by—committee active learning strategy to extract data from large datasets to maximize the diversity and avoid redundancy as relevant for neural network training to construct the QDπ dataset.

The QDπ dataset requires only 1.6 million structures to express the chemical diversity of 13 elements from the various source datasets at the ωB97M-D3(BJ)/def2-TZVPPD level of theory.

The QDπ dataset enables creation of flexible target loss functions for neural network training relevant to drug discovery, including information-dense data sets of relative conformational energies and barriers, intermolecular interactions, tautomers and relative protonation energies of drug-like compounds and biomolecular fragments.

Here we proposed and evaluated various machine learning methods for predicting pHopt, conducting extensive hyperparameter optimization and training over 11,000 model instances.

Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pHopt, including proximity of residues to the catalytic centre and the accessibility of solvent molecules.

In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach.