ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction.

BACKGROUND: Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS: In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION: The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.

STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions.

Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of drugs cause DDIs, and their interpretability is still weak. In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (substructures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-aware Tensor Neural Network model for DDI prediction (STNN-DDI). The proposed model learns a 3-D tensor of $\langle $  substructure, substructure, interaction type  $\rangle $ triplets, which characterizes a substructure-substructure interaction (SSI) space. According to a list of predefined substructures with specific chemical meanings, the mapping of drugs into this SSI space enables STNN-DDI to perform the multiple-type DDI prediction in both transductive and inductive scenarios in a unified form with an explicable manner. The comparison with deep learning-based state-of-the-art baselines demonstrates the superiority of STNN-DDI with the significant improvement of AUC, AUPR, Accuracy and Precision. More importantly, case studies illustrate its interpretability by both revealing an important substructure pair across drugs regarding a DDI type of interest and uncovering interaction type-specific substructure pairs in a given DDI. In summary, STNN-DDI provides an effective approach to predicting DDIs as well as explaining the interaction mechanisms among drugs. Source code is freely available at https://github.com/zsy-9/STNN-DDI.

Drug-drug interaction prediction with learnable size-adaptive molecular substructures.

Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.

ADENet: a novel network-based inference method for prediction of drug adverse events.

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.

Achieving Regioselectivity for Remote C-H Activation by Substructure Conformations: an Approach of Paralogous Cytochrome P450 Enzymes.

For advanced synthetic intermediates or natural products with multiple unactivated and energetically similar C(sp3)-H bonds, controlling regioselectivity for the C-H activation is particularly challenging. The use of cytochrome P450 enzymes (CYPs) is a promising solution to the 'regioelectivity' challenge in remote C-H activation. Notably, CYPs and organic catalysts share a fundamental principle: they strive to control the distance and geometry between the metal reaction center and the target C-H site. Most structural analyses of the regioselectivity of CYPs are limited to the active pocket, particularly when explaining why regioselectivity could be altered by enzyme engineering through mutagenesis. However, the substructures responsible for forming the active pocket in CYPs are well known to display complex dynamic changes and substrate-induced plasticity. In this context, we highlight a comparative study of the recently reported paralogous CYPs, IkaD and CftA, which achieve different regioselectivity towards the same substrate ikarugamycin by distinct substructure conformations. We propose that substructural conformation-controlled regioselectivity might also be present in CYPs of other natural product biosynthesis pathways, which should be considered when engineering CYPs for regioselective modifications.

Biochemical and in silico structural characterization of a cold-active arginase from the psychrophilic yeast, Glaciozyma antarctica PI12.

Glaciozyma antarctica PI12 is a psychrophilic yeast isolated from Antarctica. In this work, we describe the heterologous production, biochemical properties and in silico structure analysis of an arginase from this yeast (GaArg). GaArg is a metalloenzyme that catalyses the hydrolysis of L-arginine to L-ornithine and urea. The cDNA of GaArg was reversed transcribed, cloned, expressed and purified as a recombinant protein in Escherichia coli. The purified protein was active against L-arginine as its substrate in a reaction at 20 °C, pH 9. At 10-35 °C and pH 7-9, the catalytic activity of the protein was still present around 50%. Mn2+, Ni2+, Co2+ and K+ were able to enhance the enzyme activity more than two-fold, while GaArg is most sensitive to SDS, EDTA and DTT. The predicted structure model of GaArg showed a very similar overall fold with other known arginases. GaArg possesses predominantly smaller and uncharged amino acids, fewer salt bridges, hydrogen bonds and hydrophobic interactions compared to the other counterparts. GaArg is the first reported arginase that is cold-active, facilitated by unique structural characteristics for its adaptation of catalytic functions at low-temperature environments. The structure and function of cold-active GaArg provide insights into the potentiality of new applications in various biotechnology and pharmaceutical industries.

DAS-DDI: A dual-view framework with drug association and drug structure for drug-drug interaction prediction.

In drug development and clinical application, drug-drug interaction (DDI) prediction is crucial for patient safety and therapeutic efficacy. However, traditional methods for DDI prediction often overlook the structural features of drugs and the complex interrelationships between them, which affect the accuracy and interpretability of the model. In this paper, a novel dual-view DDI prediction framework, DAS-DDI is proposed. Firstly, a drug association network is constructed based on similarity information among drugs, which could provide rich context information for DDI prediction. Subsequently, a novel drug substructure extraction method is proposed, which could update the features of nodes and chemical bonds simultaneously, improving the comprehensiveness of the feature. Furthermore, an attention mechanism is employed to fuse multiple drug embeddings from different views dynamically, enhancing the discriminative ability of the model in handling multi-view data. Comparative experiments on three public datasets demonstrate the superiority of DAS-DDI compared with other state-of-the-art models under two scenarios.

Association of radiation dose to cardiac substructures with major ischaemic events following breast cancer radiotherapy.

BACKGROUND AND AIMS: Patients with left-sided breast cancer receive a higher mean heart dose (MHD) after radiotherapy, with subsequent risk of ischaemic heart disease. However, the optimum dosimetric predictor among cardiac substructures has not yet been determined. METHODS AND RESULTS: This study retrospectively reviewed 2158 women with breast cancer receiving adjuvant radiotherapy. The primary endpoint was a major ischaemic event. The dose-volume parameters of each delineated cardiac substructure were calculated. The risk factors for major ischaemic events and the association between MHD and major ischaemic events were analysed by Cox regression. The optimum dose-volume predictors among cardiac substructures were explored in multivariable models by comparing performance metrics of each model. At a median follow-up of 7.9 years (interquartile range 5.6-10.8 years), 89 patients developed major ischaemic events. The cumulative incidence rate of major ischaemic events was significantly higher in left-sided disease (P = 0.044). Overall, MHD increased the risk of major ischaemic events by 6.2% per Gy (hazard ratio 1.062, 95% confidence interval 1.01-1.12; P = 0.012). The model containing the volume of the left ventricle receiving 25 Gy (LV V25) with the cut-point of 4% presented with the best goodness of fit and discrimination performance in left-sided breast cancer. Age, chronic kidney disease, and hyperlipidaemia were also significant risk factors. CONCLUSION: Risk of major ischaemic events exist in the era of modern radiotherapy. LV V25 ≥ 4% appeared to be the optimum parameter and was superior to MHD in predicting major ischaemic events. This dose constraint could aid in achieving better heart protection in breast cancer radiotherapy, though a further validation study is warranted.

Worn denture teeth of a full-arch implant-supported fixed dental prosthesis: A technique to restore.

Worn denture teeth continue to be a significant complication with implant-supported prostheses. This article discusses a case report that used an intraoral scanning system to restore an existing maxillary implant-supported prosthesis with significant posterior occlusal wear.  IPS e.max (Ivoclar Vivadent, Schaan, Liechtenstein) restorations were fabricated and cemented to the prepared posterior denture teeth to re-establish the occlusal vertical dimension and to help prevent further wear of the occlusal surfaces.

PySmash: Python package and individual executable program for representative substructure generation and application.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

Computational drug repositioning based on the relationships between substructure-indication.

At present, computational methods for drug repositioning are mainly based on the whole structures of drugs, which limits the discovery of new functions due to the similarities between local structures of drugs. In this article, we, for the first time, integrated the features of chemical-genomics (substructure-domain) and pharmaco-genomics (domain-indication) based on the assumption that drug-target interactions are mediated by the substructures of drugs and the domains of proteins to identify the relationships between substructure-indication and establish a drug-substructure-indication network for predicting all therapeutic effects of tested drugs through only information on the substructures of drugs. In total, 83 205 drug-indication relationships with different correlation scores were obtained. We used three different verification methods to indicate the accuracy of the method and the reliability of the scoring system. We predicted all indications of olaparib using our method, including the known antitumor effect and unknown antiviral effect verified by literature, and we also discovered the inhibitory mechanism of olaparib toward DNA repair through its specific sub494 (o = C-C: C), as it participates in the low synthesis of the poly subfunction of the apoptosis pathway (hsa04210) by inhibiting the Inositol 1,4,5-trisphosphate receptor(s) (ITPRs) and hydrolyzing poly (ADP ribose) polymerases. ElectroCardioGrams of four drugs (quinidine, amiodarone, milrinone and fosinopril) demonstrated the effect of anti-arrhythmia. Unlike previous studies focusing on the overall structures of drugs, our research has great potential in the search for more therapeutic effects of drugs and in predicting all potential effects and mechanisms of a drug from the local structural similarity.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION: ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computational methods have been proposed for identifying potential adverse DDIs, there is still room for improvement. Existing methods are not explicitly based on the knowledge that DDIs are fundamentally caused by chemical substructure interactions instead of whole drugs' chemical structures. Furthermore, most of existing methods rely on manually engineered molecular representation, which is limited by the domain expert's knowledge.We propose substructure-substructure interaction-drug-drug interaction (SSI-DDI), a deep learning framework, which operates directly on the raw molecular graph representations of drugs for richer feature extraction; and, most importantly, breaks the DDI prediction task between two drugs down to identifying pairwise interactions between their respective substructures. SSI-DDI is evaluated on real-world data and improves DDI prediction performance compared to state-of-the-art methods. Source code is freely available at https://github.com/kanz76/SSI-DDI.

Differential alterations of amygdala nuclei volumes in acutely ill patients with anorexia nervosa and their associations with leptin levels.

BACKGROUND: The amygdala is a subcortical limbic structure consisting of histologically and functionally distinct subregions. New automated structural magnetic resonance imaging (MRI) segmentation tools facilitate the in vivo study of individual amygdala nuclei in clinical populations such as patients with anorexia nervosa (AN) who show symptoms indicative of limbic dysregulation. This study is the first to investigate amygdala nuclei volumes in AN, their relationships with leptin, a key indicator of AN-related neuroendocrine alterations, and further clinical measures. METHODS: T1-weighted MRI scans were subsegmented and multi-stage quality controlled using FreeSurfer. Left/right hemispheric amygdala nuclei volumes were cross-sectionally compared between females with AN (n = 168, 12-29 years) and age-matched healthy females (n = 168) applying general linear models. Associations with plasma leptin, body mass index (BMI), illness duration, and psychiatric symptoms were analyzed via robust linear regression. RESULTS: Globally, most amygdala nuclei volumes in both hemispheres were reduced in AN v. healthy control participants. Importantly, four specific nuclei (accessory basal, cortical, medial nuclei, corticoamygdaloid transition in the rostral-medial amygdala) showed greater volumetric reduction even relative to reductions of whole amygdala and total subcortical gray matter volumes, whereas basal, lateral, and paralaminar nuclei were less reduced. All rostral-medially clustered nuclei were positively associated with leptin in AN independent of BMI. Amygdala nuclei volumes were not associated with illness duration or psychiatric symptom severity in AN. CONCLUSIONS: In AN, amygdala nuclei are altered to different degrees. Severe volume loss in rostral-medially clustered nuclei, collectively involved in olfactory/food-related reward processing, may represent a structural correlate of AN-related symptoms. Hypoleptinemia might be linked to rostral-medial amygdala alterations.

The most common linkers in bioactive molecules and their bioisosteric replacement network.

Structures of the large majority of bioactive molecules are composed of several rings that are decorated by substituents and connected by linkers. While numerous cheminformatics studies focusing on rings and substituents are available, practically nothing has been published about the third important structural constituent of bioactive molecules - the linkers. The current study attempts to fill this gap. The most common linkers present in bioactive molecules are identified, their properties analyzed and a method for linker similarity search introduced. The bioisosteric replacement network of linkers is generated based on a large corpus of structure-activity data from medicinal chemistry literature. The results are presented in a graphical form and the underlying data are also made available for download. This analysis is intended to help medicinal chemists to better understand the role of linkers, particularly heterocyclic rings in bioactive molecules and to select an optimal set of linkers in their future project.

Classification of FLT3 inhibitors and SAR analysis by machine learning methods.

FMS-like tyrosine kinase 3 (FLT3) is a type III receptor tyrosine kinase, which is an important target for anti-cancer therapy. In this work, we conducted a structure-activity relationship (SAR) study on 3867 FLT3 inhibitors we collected. MACCS fingerprints, ECFP4 fingerprints, and TT fingerprints were used to represent the inhibitors in the dataset. A total of 36 classification models were built based on support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and deep neural networks (DNN) algorithms. Model 3D_3 built by deep neural networks (DNN) and TT fingerprints performed best on the test set with the highest prediction accuracy of 85.83% and Matthews correlation coefficient (MCC) of 0.72 and also performed well on the external test set. In addition, we clustered 3867 inhibitors into 11 subsets by the K-Means algorithm to figure out the structural characteristics of the reported FLT3 inhibitors. Finally, we analyzed the SAR of FLT3 inhibitors by RF algorithm based on ECFP4 fingerprints. The results showed that 2-aminopyrimidine, 1-ethylpiperidine,2,4-bis(methylamino)pyrimidine, amino-aromatic heterocycle, [(2E)-but-2-enyl]dimethylamine, but-2-enyl, and alkynyl were typical fragments among highly active inhibitors. Besides, three scaffolds in Subset_A (Subset 4), Subset_B, and Subset_C showed a significant relationship to inhibition activity targeting FLT3.

Forensic characteristics and genetic substructure analysis of the Handan Han population, Northern China.

We analysed the forensic characteristics and substructure of the Handan Han population based on 36 Y-STR (short tandem repeat) and Y-SNP (single nucleotide polymorphism) markers. The two most dominant haplogroups in Handan Han, O2a2b1a1a1-F8 (17.95%) and O2a2b1a2a1a (21.51%), and their abundant downstream branches, reflected the strong expansion of the precursor of the Hans in Handan. The present results enrich the forensic database and explore the genetic relationships between Handan Han and other neighbouring and/or linguistically close populations, which suggests that the current concise overview of the Han intricate substructure remains oversimplified.

locStra: Fast analysis of regional/global stratification in whole-genome sequencing studies.

locStra is an R -package for the analysis of regional and global population stratification in whole-genome sequencing (WGS) studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared with the global ones, based on user-defined window sizes and metrics, for example, the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million rare variants per subject), the runtime for the genome-wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.

Substituents of life: The most common substituent patterns present in natural products.

Comparison of substituents present in natural products with the substituents found in average synthetic molecules reveals considerable differences between these two groups. The natural products substituents contain mostly oxygen heteroatoms, are structurally more complex, often containing double bonds and are rich in stereocenters. Substituents found in synthetic molecules contain nitrogen and sulfur heteroatoms, halogenes and more aromatic and particularly heteroaromatic rings. The characteristics of substituents typical for natural products identified here can be useful in the medicinal chemistry context, for example to guide the synthesis of natural product-like libraries and natural product-inspired fragment collections. The results may be used also to support compound derivatization strategies and the design of pseudo-natural natural products.