Learning a local-variable model of aromatic and conjugated systems

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive <i>ab initio</i> quantum mechanics calculations with learned estimators. This raises questions about the representability of complex quantum chemical systems with neural networks. Can local-variable models efficiently approximate nonlocal quantum chemical features? Here, we find that convolutional architectures, those that only aggregate information locally, cannot efficiently represent aromaticity and conjugation in large systems. They cannot represent long-range nonlocality known to be important in quantum chemistry. This study uses aromatic and conjugated systems computed from molecule graphs, though reproducing quantum simulations is the ultimate goal. This task, by definition, is both computable and known to be important to chemistry. The failure of convolutional architectures on this focused task calls into question their use in modeling quantum mechanics. To remedy this heretofore unrecognized deficiency, we introduce a new architecture that propagates information back and forth in waves of nonlinear computation. This architecture is still a local-variable model, and it is both computationally and representationally efficient, processing molecules in sublinear time with far fewer parameters than convolutional networks. Wave-like propagation models aromatic and conjugated systems with high accuracy, and even models the impact of small structural changes on large molecules. This new architecture demonstrates that some nonlocal features of quantum chemistry can be efficiently represented in local variable models

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

Modeling Small Molecule Metabolism in Human Liver Microsome to Better Predict Toxicity Risk

Adverse drug reactions (ADRs) are a serious problem with increasing morbidity, mortality, and health care costs worldwide. In the U.S., ADRs are responsible for more than 50% of acute liver failure cases and are the fourth most common cause of death, costing 100,000 lives annually.Idiosyncratic adverse drug reactions (IADRs) are immune-mediated hypersensitivity ADRs that are difficult to foresee during drug development. IADRs are often caused by reactive metabolites produced during drug metabolism. These reactive metabolites covalently attach to cellular components, and the resulting conjugates may provoke toxic immune response. Because reactive metabolites are short-lived, they can be difficult to detect. Tools to reliably predict whether a compound forms reactive metabolites would enable us to avoid drug candidates prone to causing IADRs and make new medicines safer. Unfortunately, due to inadequate modeling of metabolism, current experimental and computational approaches do not reliably identify drug candidates that form reactive metabolites. Bioactivation pathways leading to reactive metabolite formations often are composed of multiple steps. To accurately predict reactive metabolite formation, we must explicitly model metabolic steps of bioactivation pathways. Therefore, we built models to predict specific metabolic transformations such as hydroxylation, epoxidation, dehydrogenation, quinonation, hydrolysis, reduction, glucuronidation, sulfuration, acetylation, and methylation. Using machine learning and literature-derived data, we trained models that can predict both the likelihood that a molecules undergoes a certain chemical transformation and the specific site(s) within the molecule where this transformation happens. Together, our metabolism models cover ∼ 95% of enzymatically-driven chemical reactions in human. Our models achieve high area under the receiver operating characteristic curve scores (AUCs) of ∼ 90% in cross-validated tests. Our mechanistic approach outperformed structural alerts—a common tool used to screen out candidate compounds during drug development. Structural alerts are chemical moieties that were frequently observed to give rise to reactive metabolite upon bioactivation. However, many safe drugs also contain structural alerts which are not bioactivated and, conversely, many toxic drugs contain no structural alert. We combined models of metabolism, metabolite structure prediction, and reactivity to offer a better prediction of reactive metabolite formation in the context of structural alerts. Based on the known bioactivation pathway(s) of each structural alert, appropriate metabolism models were applied to evaluate whether drugs containing the structural alert actually form reactive metabolites. Our study focused on the furan, phenol, nitroaromatic, and thiophene alerts. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. In addition, we used our models to uncover bioactivation mechanisms that were previously under-appreciated. For example, N-dealkylation is the oxidation of an alkylated amine at the nitrogen-carbon bond, cleaving the parent compound into an amine and an aldehyde. Even though aldehydes can be toxic, metabolic studies usually neglect to report or investigate them because they are assumed to be efficiently detoxified into carboxylic acids and alcohols. Applying the N-dealkylation model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. These results demonstrated the utility of comprehensive bioactivation models that systematically consider constituent metabolic steps in gauging toxicity risks

Atrial-selective block of sodium channels by acehytisine in rabbit myocardium

AbstractAcehytisine, a multi-ion channel blocker, can markedly inhibit INa, ICa, IKur, If at various concentrations and effectively terminate and prevent atrial fibrillation (AF) in patients and animal models, but the molecular mechanism underlying its blockage remains elusive. In this study, we investigated the effects of acehytisine on action potentials and sodium channels of atrial and ventricular myocytes isolated from rabbit, using whole-cell recording system. We found that acehytisine exerted stronger blocking effects on sodium channels in atria than in ventricles, especially at depolarization (IC50: 48.48 ± 7.75 μmol/L in atria vs. 560.17 ± 63.98 μmol/L in ventricles). It also significantly shifted steady state inactivation curves toward negative potentials in atrial myocytes, without affecting the recovery kinetics from inactivation of sodium channels in the same cells. In addition, acehytisine inhibited INa in a use-dependent manner and regulated slow inactivation kinetics by different gating configurations. These findings indicate that acehytisine selectively blocks atrial sodium channels and possesses affinity to sodium channel in certain states, which provides additional evidence for the anti-AF of acehytisine

Spatio-Temporal Evolution of Sandy Land and its Impact on Soil Wind Erosion in the Kubuqi Desert in Recent 30 Years

Continuous remote-sensing monitoring of sand in desert areas and the exploration of the spatio–temporal evolution characteristics of soil–wind erosion has an important scientific value for desertification prevention and ecological restoration. In this study, the Kubuqi Desert was selected as the study area, and the Landsat series satellite remote sensing data, supplemented by satellite remote sensing data such as GE images, SPOT-5, ZY-3, GF-1/2/6, etc., integrated object-oriented, decision tree, and auxiliary human–computer interaction interpretation methods, developed the Kubuqi Desert area dataset from 1990 to 2020, and established a soil erosion intensity database of the past 30 years based on the soil–wind erosion correction equation. The results show that the application of the training samples obtained by a high-score collaborative ground sampling to land use/cover classification in desert areas can effectively improve the efficiency of remote-sensing mapping of sand changes and the accuracy of change information identification, and the overall accuracy of the classification results is 95%. In general, the sandy area of the Kubuqi Desert area has decreased year by year, during which the mobile sand in the hinterland of the desert has expanded in a scattered distribution. The overall soil–wind erosion intensity showed a downward trend, especially since 2000; the ecological improvement trend after the implementation of desertification control projects is obvious. Changes in the sand type contributed the most to the reduction of soil–wind erosion intensity (contribution 81.14%), ecological restoration played a key role in reducing the soil–wind erosion intensity (contribution 14.42%), and the increase of forest and grass vegetation covers and agricultural oases played a positive role in solidifying the soil- and wind-proof sand fixation. The pattern of sandy land changes in desert areas is closely related to the national ecological civilization construction policy and the impact of climate change