Digital discovery最新文献

We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of calculation data, storage and retrieval of calculation results to a database, and automated handling of multi-step jobs. The program is designed to reduce the tedium and likelihood of human error for researchers of all skill-levels but is particularly targeted towards novice users who otherwise may find the barrier to entry to computational chemistry unnecessarily high. To date, this program has been used to successfully run and analyse over 50 000 individual calculations, evidencing its usefulness and utility. The Digichem program is presently released under a free-to-use license, and components of the Digichem system are additionally available under an open-source license.

How much chemistry can be described by looking only at each atom, its neighbours and its next-nearest neighbours? We present a method for predicting reaction sites based only on a simple, two-bond model. Machine learning classification models were trained and evaluated using atom-level labels and descriptors, including bond strength and connectivity. Despite limitations in covering only local chemical environments, the models achieved over 80% accuracy even with challenging datasets that cover a diverse chemical space. Whilst this simplistic model is necessarily incomplete, it describes a large amount of interesting chemistry.

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An external chemical substance (which may be a medicinal drug or an exposome), after ingestion, undergoes a series of dynamic movements and metabolic alterations known as pharmacokinetic events while exerting different physiological actions on the body (pharmacodynamics events). Plasma protein binding and hepatocyte intrinsic clearance are crucial pharmacokinetic events that influence the efficacy and safety of a chemical substance. Plasma protein binding determines the fraction of a chemical compound bound to plasma proteins, affecting the distribution and duration of action of the compound. The compounds with high protein binding may have a smaller free fraction available for pharmacological activity, potentially altering their therapeutic effects. On the other hand, hepatocyte intrinsic clearance represents the liver's capacity to eliminate a chemical compound through metabolism. It is a critical determinant of the elimination half-life of the chemical substance. Understanding hepatic clearance is essential for predicting chemical toxicity and designing safety guidelines. Recently, the huge expansion of computational resources has led to the development of various in silico models to generate predictive models as an alternative to animal experimentation. In this research work, we developed different types of machine learning (ML) based quantitative structure–activity relationship (QSAR) models for the prediction of the compound's plasma protein fraction unbound values and hepatocyte intrinsic clearance. Here, we have developed regression-based models with the protein fraction unbound (f_u) human data set (n = 1812) and a classification-based model with the hepatocyte intrinsic clearance (Cl_int) human data set (n = 1241) collected from the recently published ICE (Integrated Chemical Environment) database. We have further analyzed the influence of the plasma protein binding on the hepatocyte intrinsic clearance, by considering the compounds having both types of target variable values. For the fraction unbound data set, the support vector machine (SVM) model shows superior results compared to other models, but for the hepatocyte intrinsic clearance data set, random forest (RF) shows the best results. We have further made predictions of these important pharmacokinetic parameters through the similarity-based read-across (RA) method. A Python-based tool for predicting the endpoints has been developed and made available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/pkpy-tool.

Generative models have received significant attention in recent years for materials science applications, particularly in the area of inverse design for materials discovery. However, these models are usually assessed based on newly generated, unverified materials, using heuristic metrics such as charge neutrality, which provide a narrow evaluation of a model's performance. Also, current efforts for inorganic materials have predominantly focused on small, periodic crystals (≤20 atoms), even though the capability to generate large, more intricate and disordered structures would expand the applicability of generative modeling to a broader spectrum of materials. In this work, we present the Disordered Materials & Interfaces Benchmark (Dismai-Bench), a generative model benchmark that uses datasets of disordered alloys, interfaces, and amorphous silicon (256–264 atoms per structure). Models are trained on each dataset independently, and evaluated through direct structural comparisons between training and generated structures. Such comparisons are only possible because the material system of each training dataset is fixed. Benchmarking was performed on two graph diffusion models and two (coordinate-based) U-Net diffusion models. The graph models were found to significantly outperform the U-Net models due to the higher expressive power of graphs. While noise in the less expressive models can assist in discovering materials by facilitating exploration beyond the training distribution, these models face significant challenges when confronted with more complex structures. To further demonstrate the benefits of this benchmarking in the development process of a generative model, we considered the case of developing a point-cloud-based generative adversarial network (GAN) to generate low-energy disordered interfaces. We tested different GAN architectures and identified reasons for good/poor performance. We show that the best performing architecture, CryinGAN, outperforms the U-Net models, and is competitive against the graph models despite its lack of invariances and weaker expressive power. This work provides a new framework and insights to guide the development of future generative models, whether for ordered or disordered materials.