Journal: Journal of Chemical Theory and Computation

Journal: Digital Discovery

Journal: Chemical Science

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

Journal: Journal of Chemical Information and Modeling

Proteochemometric models (PCMs) are used in computational drug discovery to employ both protein and ligand representations jointly for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often serving as a basis for scoring functions, rigorous evaluation standards have not always been consistently applied. In this study, using kinase-ligand bioactivity prediction as a model system, we highlight the critical roles of data set curation, permutation testing, class imbalances, and various data splitting strategies for mitigating plausible data leakage and embedding quality in determining model performance. Our findings indicate that data splitting and class imbalances are the most critical factors affecting PCM performance, emphasizing the challenges in the generalizing ability of ML/DL-PCMs. We evaluated various protein–ligand descriptors and embeddings, including those augmented with multiple sequence alignment information. However, permutation testing consistently demonstrated that protein embeddings contributed minimally to PCM efficacy. This study advocates for the adoption of stringent evaluation standards to enhance the generalizability of models to out-of-distribution data and improve benchmarking practices.

Journal: Journal of Chemical Theory and Computation

Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving several concerted bond changes─as can be found in many key steps of natural product syntheses─can complicate the search process. To mitigate these complications, we present a mechanism search strategy particularly suited to help expedite exploration of an exemplary family of such complex reactions, cyclizations. We provide a cost-effective strategy for identifying relevant elementary reaction steps by combining graph-based enumeration schemes and machine learning techniques for intermediate filtering. Key to this approach is our use of a neural network potential (NNP), AIMNet2-rxn, for computational evaluation of each candidate reaction pathway. In this article, we evaluate the NNP's ability to estimate activation energies, demonstrate the correct anticipation of stereoselectivity, and recapitulate complex enabling steps in natural product synthesis.

Journal: Proceedings of the 34th ACM International Conference on Information and Knowledge Management

Reaction yield prediction underpins computer-aided synthesis prediction (CASP). Formulated as a regression problem that takes both reactants and products as input, this task has been extensively studied using machine learning methods, based on handcrafted fingerprint features, SMILES encoded by Transformers, and molecular graphs encoded by Graph Neural Networks. However, a major limitation of these methods is their inability to effectively capture and model the underlying uncertainties, arising both from the inherently stochastic nature of chemical reaction processes and from inconsistencies or noise in how yields are measured and reported. What makes this seemingly simple regression problem even more challenging is the lack of any principled way to account for the underlying uncertainties, due to missing or unrecorded experimental process (commonly happens in chemical labs). Given these challenges, we propose a new formulation for yield prediction. Rather than assuming a single deterministic yield value for a given reaction, we model the outcome as a probabilistic distribution over three discrete yield regimes: high, medium, and low, reflecting the inherent uncertainty in the reaction process, which is often only partially observed. Accordingly, we propose Proto-Yield, an encoder-agnostic prototype network that models reactions as occurring in one of three yield regimes: high, medium, or low. Without access to full reaction processes, Proto-Yield learns to infer latent regimes and their associated yield distributions from noisy, incomplete training data. During inference, Proto-Yield outputs both a calibrated probability distribution over the yield regimes and the predicted yield conditioned on each regime. Extensive experiments on a 41,000-reaction patent corpus and two high-throughput benchmarks show that Proto-Yield improves R2 by up to 15% and reduces RMSE/MAE by 13% compared to baseline methods.

Journal: Digital Discovery

Autonomous experiments are vulnerable to unforeseen adverse events. We developed a transferable ML framework that flags affected HPLC runs in real time and provides expert-level quality control without human oversight.

Journal: Nature Computational Science

As quantum mechanics marks its centennial in 2025, machine learning interatomic potentials have emerged as transformative tools in molecular modeling, bridging quantum mechanical accuracy with classical efficiency. Here we examine their development through four defining challenges-achieving chemical accuracy, maintaining computational efficiency, ensuring interpretability and reaching universal generalizability. We highlight architectural innovations, physics-informed approaches, and foundation models trained on extensive data. Together, these developments chart a path toward predictive, transferable and physically grounded machine learning frameworks for next-generation computational chemistry.

Journal: Angewandte Chemie International Edition

Journal: JACS Au

Ring strain energy (RSE) is crucial for understanding molecular reactivity, with broad implications in polymerization, click chemistry, drug discovery and beyond. However, quantitatively determining RSE through experiments or quantum mechanics (QM) is resource-intensive, limiting its application on a large scale. We present a machine learning (ML)-based workflow that enables the reliable and efficient prediction of RSE, entirely bypassing traditional QM calculations. Our workflow employs AIMNet2 machine learning interatomic potentials and Auto3D for the identification of low-energy conformers and RSE computation. Remarkably, it achieves an R 2 of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol when benchmarked against the ωB97M-D4/Def2-TZVPP method, while running orders of magnitude faster than DFT calculations. To demonstrate the utility of our workflow, we successfully differentiated reactive from nonreactive molecules in copper-free click chemistry, [3 + 2] cycloaddition reaction and ring-opening metathesis polymerization, underscoring its transferability to diverse molecular systems. Additionally, we compiled the RSE Atlas, a computational database encompassing 16,905 single-ring molecules, offering a valuable resource for investigating factors influencing RSE. Our approach transforms RSE into a readily computable property, facilitating its integration into reaction designs.

Journal: Crystal Growth & Design

Identifying thermodynamically stable crystal structures remains a key challenge in materials chemistry. Computational crystal structure prediction (CSP) workflows typically rank candidate structures by lattice energy to assess relative stability. Approaches using self-consistent first-principles calculations become prohibitively expensive, especially when millions of energy evaluations are required for complex molecular systems with many atoms per unit cell. Here, we provide a detailed analysis of our methodology and results from the seventh blind test of crystal structure prediction organized by the Cambridge Crystallographic Data Centre (CCDC). We present an approach that significantly accelerates CSP by training target-specific machine-learned interatomic potentials (MLIPs). AIMNet2 MLIPs are trained on density functional theory (DFT) calculations of molecular clusters, herein referred to as n-mers. We demonstrate that potentials trained on gas phase dispersion-corrected DFT reference data of n-mers successfully extend to crystalline environments, accurately characterizing the CSP landscape and correctly ranking structures by relative stability. Our methodology effectively captures the underlying physics of thermodynamic crystal stability using only molecular cluster data, avoiding the need for expensive periodic calculations. The performance of target-specific AIMNet2 interatomic potentials is illustrated across diverse chemical systems relevant to pharmaceutical, optoelectronic, and agrochemical applications, demonstrating their promise as efficient alternatives to full DFT calculations for routine CSP tasks.

Journal: Digital Discovery

Revisiting GEOM drugs: corrected metrics and novel energy-based structural benchmark enable rigorous evaluation of 3D molecule generative models.

Journal: Angewandte Chemie

Journal: Journal of Chemical Theory and Computation

Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions under diverse conditions and the prediction of reaction properties, such as barrier heights. This work introduces ANI-1xBB, a novel ANI-based reactive ML potential trained on off-equilibrium molecular conformers generated through an automated bond-breaking workflow. ANI-1xBB significantly enhances the prediction of reaction energetics, barrier heights, and bond dissociation energies, surpassing those of conventional ANI models. Our results show that ANI-1xBB improves transition state modeling and reaction pathway prediction while generalizing effectively to pericyclic reactions and radical-driven processes. Furthermore, the automated data generation strategy supports the efficient construction of large-scale, high-quality reactive data sets, reducing reliance on expensive QM calculations. This work highlights ANI-1xBB as a practical model for accelerating the development of reactive machine learning potentials, offering new opportunities for modeling reaction phenomena.

Journal: Journal of Chemical Information and Modeling

Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks.

Journal: Chemical Science

Ring Vault contains 201 546 cyclic molecules across 11 elements. AIMNet2 with 3D information outperformed 2D models in predicting the electronic properties of cyclic molecules.

Journal: Journal of Chemical Theory and Computation

Journal: Chemical Science

In this work, we combined Deep Docking and free energy MD simulations for the in silico screening and experimental validation for potential inhibitors of leucine rich repeat kinase 2 (LRRK2) targeting the WD40 repeat (WDR) domain.

Journal: Journal of Chemical Theory and Computation

Deep learning Neural Networks (NN) have been developed in the field of molecular modeling for the purpose of circumventing the high computational cost of quantum-mechanical calculations while rivaling their accuracies. Although these networks have found great success, they generally lack the ability to accurately describe long-range interactions, which makes them unusable for extended molecular systems. Herein, we provide a method for partially retraining the deep learning general-use neural network ANI, in which the long-range interactions are represented via atomic electrostatic potentials. The electrostatic potentials, generated with polarizable effective fragment potentials (EFP), are used as an additional input feature for the network. This new ANI/EFP network can predict solute-solvent interaction energies on a trained data set with a kcal/mol accuracy. It also shows promise in predicting the interaction energies of a solute in solvent environments that have not been included in a training data set. The proposed protocol can be taken as an example and further developed, leading to highly accurate and transferable neural network potentials capable of handling long-range interactions and extended molecular systems.

Journal: Journal of the American Chemical Society

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that aid in predicting the crystallization behavior of organic thin films.