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Showing 861 to 864 of 5186 entries
Published on May 6, 2022
UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning.
Abstract: Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.
Published on May 6, 2022
Supersaturation-Based Drug Delivery Systems: Strategy for Bioavailability Enhancement of Poorly Water-Soluble Drugs.
Abstract: At present, the majority of APIs synthesized today remain challenging tasks for formulation development. Many technologies are being utilized or explored for enhancing solubility, such as chemical modification, novel drug delivery systems (microemulsions, nanoparticles, liposomes, etc.), salt formation, and many more. One promising avenue attaining attention presently is supersaturated drug delivery systems. When exposed to gastrointestinal fluids, drug concentration exceeds equilibrium solubility and a supersaturation state is maintained long enough to be absorbed, enhancing bioavailability. In this review, the latest developments in supersaturated drug delivery systems are addressed in depth.
Published on May 5, 2022
A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction.
Abstract: BACKGROUND: To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. RESULTS: We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. CONCLUSIONS: Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.
Published on May 4, 2022
Network Pharmacology Approach for Medicinal Plants: Review and Assessment.
Abstract: Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.
Published on May 4, 2022
Cyclobutanes in Small-Molecule Drug Candidates.
Abstract: Cyclobutanes are increasingly used in medicinal chemistry in the search for relevant biological properties. Important characteristics of the cyclobutane ring include its unique puckered structure, longer C-C bond lengths, increased C-C pi-character and relative chemical inertness for a highly strained carbocycle. This review will focus on contributions of cyclobutane rings in drug candidates to arrive at favorable properties. Cyclobutanes have been employed for improving multiple factors such as preventing cis/trans-isomerization by replacing alkenes, replacing larger cyclic systems, increasing metabolic stability, directing key pharmacophore groups, inducing conformational restriction, reducing planarity, as aryl isostere and filling hydrophobic pockets.
Published on May 4, 2022
Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.
Abstract: BACKGROUND: Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations. RESULTS: In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes' neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA. CONCLUSIONS: Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.
Published on May 4, 2022
Discovery of adapalene and dihydrotachysterol as antiviral agents for the Omicron variant of SARS-CoV-2 through computational drug repurposing.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been significantly paralyzing the societies, economies and health care systems around the globe. The mutations on the genome of SARS-CoV-2 led to the emergence of new variants, some of which are classified as "variant of concern" due to their increased transmissibility and better viral fitness. The Omicron variant, as the latest variant of concern, dominated the current COVID-19 cases all around the world. Unlike the previous variants of concern, the Omicron variant has 15 mutations on the receptor-binding domain of spike protein and the changes in the key amino acid residues of S protein can enhance the binding ability of the virus to hACE2, resulting in a significant increase in the infectivity of the Omicron variant. Therefore, there is still an urgent need for treatment and prevention of variants of concern, particularly for the Omicron variant. In this study, an in silico drug repurposing was conducted through the molecular docking of 2890 FDA-approved drugs against the mutant S protein of SARS-CoV-2 for Omicron variant. We discovered promising drug candidates for the inhibition of alarming Omicron variant such as quinestrol, adapalene, tamibarotene, and dihydrotachysterol. The stability of ligands complexed with the mutant S protein was confirmed using MD simulations. The lead compounds were further evaluated for their potential use and side effects based on the current literature. Particularly, adapalene, dihydrotachysterol, levocabastine and bexarotene came into prominence due to their non-interference with the normal physiological processes. Therefore, this study suggests that these approved drugs can be considered as drug candidates for further in vitro and in vivo studies to develop new treatment options for the Omicron variant of SARS-CoV-2.
Published on May 3, 2022
Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond.
Abstract: Cyclic peptides formed by disulfide bonds have been one large group of common drug candidates in drug development. Structural information of a peptide is essential to understand its interaction with its target. However, due to the high flexibility of peptides, it is difficult to sample the near-native conformations of a peptide. Here, we have developed an extended version of our MODPEP approach, named MODPEP2.0, to fast generate the conformations of cyclic peptides formed by a disulfide bond. MODPEP2.0 builds the three-dimensional (3D) structures of a cyclic peptide from scratch by assembling amino acids one by one onto the cyclic fragment based on the constructed rotamer and cyclic backbone libraries. Being tested on a data set of 193 diverse cyclic peptides, MODPEP2.0 obtained a considerable advantage in both accuracy and computational efficiency, compared with other sampling algorithms including PEP-FOLD, ETKDG, and modified ETKDG (mETKDG). MODPEP2.0 achieved a high sampling accuracy with an average C[Formula: see text] RMSD of 2.20 A and 1.66 A when 10 and 100 conformations were considered, respectively, compared with 3.41 A and 2.62 A for PEP-FOLD, 3.44 A and 3.16 A for ETKDG, 3.09 A and 2.72 A for mETKDG. MODPEP2.0 also reproduced experimental peptide structures for 81.35% of the test cases when an ensemble of 100 conformations were considered, compared with 54.95%, 37.50% and 50.00% for PEP-FOLD, ETKDG, and mETKDG. MODPEP2.0 is computationally efficient and can generate 100 peptide conformations in one second. MODPEP2.0 will be useful in sampling cyclic peptide structures and modeling related protein-peptide interactions, facilitating the development of cyclic peptide drugs.
Published on May 2, 2022
Single-Cell Protein and Transcriptional Characterization of Epiretinal Membranes From Patients With Proliferative Vitreoretinopathy.
Abstract: Purpose: Proliferative vitreoretinopathy (PVR) remains an unresolved clinical challenge and can lead to frequent revision surgery and blindness vision loss. The aim of this study was to characterize the microenvironment of epiretinal PVR tissue, in order to shed more light on the complex pathophysiology and to unravel new treatment options. Methods: A total of 44 tissue samples were analyzed in this study, including 19 epiretinal PVRs, 13 epiretinal membranes (ERMs) from patients with macular pucker, as well as 12 internal limiting membranes (ILMs). The cellular and molecular microenvironment was assessed by cell type deconvolution analysis (xCell), RNA sequencing data and single-cell imaging mass cytometry. Candidate drugs for PVR treatment were identified in silico via a transcriptome-based drug-repurposing approach. Results: RNA sequencing of tissue samples demonstrated distinct transcriptional profiles of PVR, ERM, and ILM samples. Differential gene expression analysis revealed 3194 upregulated genes in PVR compared with ILM, including FN1 and SPARC, which contribute to biological processes, such as extracellular matrix (ECM) organization. The xCell and IMC analyses showed that PVR membranes were composed of macrophages, retinal pigment epithelium, and alpha-SMA-positive myofibroblasts, the latter predominantly characterized by the co-expression of immune cell signature markers. Finally, 13 drugs were identified as potential therapeutics for PVR, including aminocaproic acid and various topoisomerase-2A inhibitors. Conclusions: Epiretinal PVR membranes exhibit a unique and complex transcriptional and cellular profile dominated by immune cells and myofibroblasts, as well as a variety of ECM components. Our findings provide new insights into the pathophysiology of PVR and suggest potential targeted therapeutic options.
Published in April 2022
Computer-designed repurposing of chemical wastes into drugs.
Abstract: As the chemical industry continues to produce considerable quantities of waste chemicals(1,2), it is essential to devise 'circular chemistry'(3-8) schemes to productively back-convert at least a portion of these unwanted materials into useful products. Despite substantial progress in the degradation of some classes of harmful chemicals(9), work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas(10-15). Comprehensive analyses of which valuable products are synthesizable from diverse chemical wastes are difficult because even small sets of waste substrates can, within few steps, generate millions of putative products, each synthesizable by multiple routes forming densely connected networks. Tracing all such syntheses and selecting those that also meet criteria of process and 'green' chemistries is, arguably, beyond the cognition of human chemists. Here we show how computers equipped with broad synthetic knowledge can help address this challenge. Using the forward-synthesis Allchemy platform(16), we generate giant synthetic networks emanating from approximately 200 waste chemicals recycled on commercial scales, retrieve from these networks tens of thousands of routes leading to approximately 300 important drugs and agrochemicals, and algorithmically rank these syntheses according to the accepted metrics of sustainable chemistry(17-19). Several of these routes we validate by experiment, including an industrially realistic demonstration on a 'pharmacy on demand' flow-chemistry platform(20). Wide adoption of computerized waste-to-valuable algorithms can accelerate productive reuse of chemicals that would otherwise incur storage or disposal costs, or even pose environmental hazards.
Showing 861 to 864 of 5186 entries