Deep Learning Based Drug Metabolites Prediction

Distinct Species-Specific and Toxigenic Metabolic Profiles for 6PPD and 6PPD Quinone by P450 Enzymes: Insights from In Vitro and In Silico Studies

The tire rubber antioxidant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) and its quinone product (6PPDQ) are prevalent emerging contaminants, yet their biotransformation profiles remain poorly understood, hampering the assessment of environmental and health risks. This study investigated the phase-I metabolism of 6PPD and 6PPDQ across aquatic and mammalian species through in vitro liver microsome (LM) incubations and in silico simulations. A total of 40 metabolites from seven pathways were identified using the highly sensitive nano-electrospray ionization mass spectrometry. Notably, 6PPDQ was consistently detected as a 6PPD metabolite with an approximate 2% yield, highlighting biotransformation as a neglected indirect exposure pathway for 6PPDQ in organisms. 6PPDQ was calculated to form through a facile two-step phenyl hydroxylation of 6PPD, catalyzed by cytochrome P450 enzymes. Distinct species-specific metabolic kinetics were observed, with fish LM demonstrating retarded biotransformation rates for 6PPD and 6PPDQ compared to mammalian LM, suggesting the vulnerability of aquatic vertebrates to these contaminants. Intriguingly, two novel coupled metabolites were identified for 6PPD, which were predicted to exhibit elevated toxicity compared to 6PPDQ and result from C-N oxidative coupling by P450s. These unveiled metabolic profiles offer valuable insights for the risk assessment of 6PPD and 6PPDQ, which may inform future studies and regulatory actions.

Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human

Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of drugs with targeting metabolic pathways in the organisms can provide a more complete understanding of the metabolic effects of a drug and help to identify potential drug-drug interactions. In this study, we proposed a machine learning hybrid model Graph Transformer Integrated Encoder (GTIE-RT) for mapping drugs to target metabolic pathways in human. The proposed model is a composite of a Graph Convolution Network (GCN) and transformer encoder for graph embedding and attention mechanism. The output of the transformer encoder is then fed into the Extremely Randomized Trees Classifier to predict target metabolic pathways. The evaluation of the GTIE-RT on drugs dataset demonstrates excellent performance metrics, including accuracy (>95%), recall (>92%), precision (>93%) and F1-score (>92%). Compared to other variants and machine learning methods, GTIE-RT consistently shows more reliable results.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

The Combination of Electrochemistry and Microfluidic Technology in Drug Metabolism Studies

Drugs are metabolized within the liver (pH 7.4) by phase I and phase II metabolism. During the process, reactive metabolites can be formed that react covalently with biomolecules and induce toxicity. Identifying and detecting reactive metabolites is an important part of drug development. Preclinical and clinical investigations are conducted to assess the toxicity and safety of a new drug candidate. Electrochemistry coupled to mass spectrometry is an ideal complementary technique to the current preclinical studies, a pure instrumental approach without any purification steps and tedious protocols. The combination of microfluidics with electrochemistry towards the mimicry of drug metabolism offers portability, low volume of reagents and faster reaction times. This review explores the development of microfluidic electrochemical cells for mimicking drug metabolism.

Structural Modification in Anesthetic Drug Development for Prodrugs and Soft Drugs

Among the advancements in drug structural modifications, the increased focus on drug metabolic and pharmacokinetic properties in the anesthetic drug design process has led to significant developments. Drug metabolism also plays a key role in optimizing the pharmacokinetics, pharmacodynamics, and safety of drug molecules. Thus, in the field of anesthesiology, the applications of pharmacokinetic strategies are discussed in the context of sedatives, analgesics, and muscle relaxants. In this review, we summarize two approaches for structural optimization to develop anesthetic drugs, by designing prodrugs and soft drugs. Drugs that both failed and succeeded during the developmental stage are highlighted to illustrate how drug metabolism and pharmacokinetic optimization strategies may help improve their physical and chemical properties.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Deep learning tools for advancing drug discovery and development

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug-target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Deep metabolome: Applications of deep learning in metabolomics

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.