Simplified Molecular Input Line Entry Research Articles

S-COPHY: A deep learning model for predicting the chemical class of compounds as cosmetics or pharmaceuticals based on single 3D molecular images

Non-animal-based in vitro and in silico approaches for the safety assessment of cosmetic ingredients, recently referred to as Next Generation Risk Assessment (NGRA)/New Approach Methodologies (NAMs), are evolving rapidly as approaches to provide a basis for the regulatory acceptance of new materials. However, predictive models should be applied only to chemicals within the chemical space defined by the dataset used in generating the model. Thus, only predictions for new molecules that are relatively similar to the modeling set can considered reliable with strong confidence. In this study, we developed the S-COPHY model, which employs deep learning to classify new compounds based on their structural similarity to a large collection of pharmaceutical and cosmetic compounds. S-COPHY shows high predictive accuracy both internally and externally, and in particular, there were only a few instances where pharmaceuticals were incorrectly predicted as cosmetics. The use of deep learning enabled the automatic generation of input data from SMILES (Simplified Molecular Input Line Entry System) information, resulting in more consistent model outcomes. Furthermore, GRAD-CAM (Gradient-weighted Class Activation Map) analysis provided insights into the specific structures that contribute to the model's predictions. The potentiality of S-COPHY to identify characteristic structures associated with pharmaceutical-like activity indicates its potential value in supporting safety assessments of cosmetic ingredients. Our results indicate that the S-COPHY model is a promising approach to support decision-making in large chemical spaces, thereby contributing to the safety evaluation of cosmetic ingredients. Expansion of the model to other categories, such as pesticides, could further extend its applicability.

Geometry-Based Molecular Generation With Deep Constrained Variational Autoencoder.

Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based on geometric representation for molecular generation with specific properties, which is protein-context-dependent. In terms of machine learning, it includes continuous feature embedding encoder and molecular generation decoder. Our key contribution is to propose an efficient geometric embedding method, including the spatial structure representations of drug molecule (converting the 3-D coordinates into image) and the geometric graph representations of protein target (modeling the protein surface as a mesh). The 3-D geometric information is vital to successful molecular generation, which is different from previous molecular generative methods based on 1-D or 2-D. Our model framework generates specific molecules in two phases, by first generating special image with molecular 3-D information to learn latent representations and generating molecules with constrained condition based on geometric graph convolution for specific protein and then inputting the generated structural molecules into a parser network for obtaining Simplified Molecular Input Line Entry System (SMILES) strings. Our model achieves competitive performance that implies its potential effectiveness to enable the exploration of the vast chemical space for drug discovery.

Invalid SMILES are beneficial rather than detrimental to chemical language models

Generative machine learning models have attracted intense interest for their ability to sample novel molecules with desired chemical or biological properties. Among these, language models trained on SMILES (Simplified Molecular-Input Line-Entry System) representations have been subject to the most extensive experimental validation and have been widely adopted. However, these models have what is perceived to be a major limitation: some fraction of the SMILES strings that they generate are invalid, meaning that they cannot be decoded to a chemical structure. This perceived shortcoming has motivated a remarkably broad spectrum of work designed to mitigate the generation of invalid SMILES or correct them post hoc. Here I provide causal evidence that the ability to produce invalid outputs is not harmful but is instead beneficial to chemical language models. I show that the generation of invalid outputs provides a self-corrective mechanism that filters low-likelihood samples from the language model output. Conversely, enforcing valid outputs produces structural biases in the generated molecules, impairing distribution learning and limiting generalization to unseen chemical space. Together, these results refute the prevailing assumption that invalid SMILES are a shortcoming of chemical language models and reframe them as a feature, not a bug.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

MotivationThe prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.ResultsAccurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

Abstract B023: Modeling of new drugs clinical trials outcome with patients’ digital twins cohorts

Abstract There is a problem of clinical trial failure, as each new drug should surpass the effectiveness of existing treatment regimens, which becomes increasingly challenging over time. Another significant issue is treating patients who have developed resistance to the current therapies. Essentially, the use of drug combinations or off-label drug use, where the indication does not match the diagnosis, is akin to an experiment, as there is insufficient data on which drug or combination to use. This work proposes an approach utilizing computer modeling of patients using gene expression and clinical data. Deep learning and generative adversarial networks are employed as modeling tools. The training data for the algorithms were sourced from publicly available databases such as TCGA, Drugbank, CCLE, and GDSC. The modeling is based on the hypothesis of similarity between patients, similarity between drugs, as well as the similarity between individual organs and patient tissues with cell lines, with similarity being computed mathematically. As a result, a patient model is created, where the input consists of drugs and their combinations, and the output provides survival probability values. These model data can be generated in any required quantity with generative adversarial networks (GAN) technology to create observation and control groups. Consequently, it becomes possible to simulate clinical trials, forecasting their outcomes, and, most importantly, optimizing the trial parameters to maximize the likelihood of success. We obtained patient data and drug information from the TCGA (The Cancer Genome Atlas) database. Biologicals were excluded, and only small molecules, including targeted therapies and chemotherapeutic agents, were retained. The dataset consisted of information on 3225 patients and the effects of 161 drugs. According to our hypothesis, the patient's profile includes three groups of factors, and each of these groups contributes approximately equally to the duration of overall survival (OS) and disease-free survival (DFS) intervals. The first group comprises clinical data such as age, diagnosis, and disease stage. The second group consists of genetic data, specifically gene expression profiles, which capture the functional characteristics of genes. The third group represents the drug or combination of drugs used for patient treatment. Therefore, it is necessary to ensure that all three groups of factors have comparable dimensions of influence. For drug data, we obtained information from the DrugBank database. We represented the drug molecules using SMILES (Simplified Molecular Input Line Entry System) notation, which were then converted into 100-dimensional vectors using embedding techniques derived from natural language processing technologies. To represent combinations of drugs, we employed vector addition of the individual drug vectors. As the outcome measure for prediction, we considered numerical values representing the durations of OS and DFS intervals for each respective task. Citation Format: Dmitrii K. Chebanov, Vsevolod A. Misyurin. Modeling of new drugs clinical trials outcome with patients’ digital twins cohorts [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B023.

Integration of the Natural Language Processing of Structural Information Simplified Molecular-Input Line-Entry System Can Improve the In Vitro Prediction of Human Skin Sensitizers.

Natural language processing (NLP) technology has recently used to predict substance properties based on their Simplified Molecular-Input Line-Entry System (SMILES). We aimed to develop a model predicting human skin sensitizers by integrating text features derived from SMILES with in vitro test outcomes. The dataset on SMILES, physicochemical properties, in vitro tests (DPRA, KeratinoSensTM, h-CLAT, and SENS-IS assays), and human potency categories for 122 substances sourced from the Cosmetics Europe database. The ChemBERTa model was employed to analyze the SMILES of substances. The last hidden layer embedding of ChemBERTa was tested with other features. Given the modest dataset size, we trained five XGBoost models using subsets of the training data, and subsequently employed bagging to create the final model. Notably, the features computed from SMILES played a pivotal role in the model for distinguishing sensitizers and non-sensitizers. The final model demonstrated a classification accuracy of 80% and an AUC-ROC of 0.82, effectively discriminating sensitizers from non-sensitizers. Furthermore, the model exhibited an accuracy of 82% and an AUC-ROC of 0.82 in classifying strong and weak sensitizers. In summary, we demonstrated that the integration of NLP of SMILES with in vitro test results can enhance the prediction of health hazard associated with chemicals.

Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the deep learning method. For this purpose, a Trained Neural Network (TNN) was used to produce 75 molecules similar to Favipiravir by using Simplified Molecular Input Line Entry System (SMILES) representations. The binding properties of molecules to Viral RNA-dependent RNA polymerase (RdRp) were studied by using molecular docking studies. To confirm the accuracy of this method, compounds were also tested against 3CL protease (3CLpro), which is another important enzyme for the progression of SARS-CoV-2. Compounds having better binding energies and RMSD values than favipiravir were searched with similarity analysis on the ChEMBL drug database in order to find similar structures with RdRp and 3CLpro inhibitory activities. A similarity search found new 200 potential RdRp and 3CLpro inhibitors structurally similar to produced molecules, and these compounds were again evaluated for their receptor interactions with molecular docking studies. Compounds showed better interaction with RdRp protease than 3CLpro. This result presented that artificial intelligence correctly produced structures similar to favipiravir that act more specifically as RdRp inhibitors. In addition, Lipinski's rules were applied to the molecules that showed the best interaction with RdRp, and 7 compounds were determined to be potential drug candidates. Among these compounds, a Molecular Dynamic simulation study was applied for ChEMBL ID:1193133 to better understand the existence and duration of the compound in the receptor site. The results confirmed that the ChEMBL ID:1193133 compound showed good Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bonding, and remaining time in the active site; therefore, it was considered that it could be active against the virus. This compound was also tested for antiviral activity, and it was determined that it did not delay viral infection, although it was cytotoxic between 5mg/mL-1.25mg/mL concentrations. However, if other compounds could be tested, it might provide a chance to obtain activity, and compounds should also be tested against the enzymes as well as the other types of viruses.

Pesti-DGI-Net: A multi-modal deep learning architecture based on dual interpretability for pesticide-likeness prediction

“Pesticide-likeness” represents an extension of the pharmaceutical concept of “drug-likeness” to the field of pesticides. The development of algorithmic tools for predicting pesticide-likeness holds great significance for the rational design of pesticide molecules. Among various computational approaches, artificial intelligence techniques, especially deep learning, stand out due to their distinctive advantages. However, the application of deep learning models in the field of pesticide-likeness remains relatively limited. To address this gap, we proposed a multi-modal deep learning architecture, termed Pesti-DGI-Net, which took the standard Simplified Molecular Input Line Entry System (SMILES) of compounds as input and combined molecular representations across multiple dimensions. Through this fusion, Pesti-DGI-Net made accurate predictions of the pesticide-likeness for candidate compounds, as substantiated by extensive evaluations on internal test sets and an external independent test set. Additionally, Pesti-DGI-Net provided two interpretable methods to elucidate the relationship between chemical structure and pesticide-likeness. Comparison with domain experts showed that Pesti-DGI-Net enabled researchers to better understand the prediction results. Finally, we integrated Pesti-DGI-Net with existing web resources to comprehensively assess the potential of compounds as pesticide-like molecules. Our cloud platform is freely available at http://chemyang.ccnu.edu.cn/ccb/server/CoPLE/.

SG-ATT: A Sequence Graph Cross-Attention Representation Architecture for Molecular Property Prediction.

Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties of molecules are complex, and a single encoding of molecular features from SMILES sequences or molecular graph structures cannot adequately represent molecular information. Aiming to address this problem, this study proposes a sequence graph cross-attention (SG-ATT) representation architecture for a molecular property prediction model to efficiently use domain knowledge to enhance molecular graph feature encoding and combine the features of molecular SMILES sequences. The SG-ATT fuses the two-dimensional molecular features so that the current model input molecular information contains molecular structure information and semantic information. The SG-ATT was tested on nine molecular property prediction tasks. Among them, the biggest SG-ATT model performance improvement was 4.5% on the BACE dataset, and the average model performance improvement was 1.83% on the full dataset. Additionally, specific model interpretability studies were conducted to showcase the performance of the SG-ATT model on different datasets. In-depth analysis was provided through case studies of in vitro validation. Finally, network tools for molecular property prediction were developed for the use of researchers.

Effects of tea catechin extracts from BB35 and purple (TRFK 306) tea clones on the gene expression of Egfr, App, Bcl2, Dnmt, Casp3, Hif1a, Gadd45b and Psmb5 genes involved in triple negative breast cancer diseases: In silico and in vitro study

Tea has been shown to contain metabolites that exhibit antioxidant, anti-inflammatory, anti-diabetic, anti-cancer and anti-cardio vascular diseases properties. This study aimed to carry out an in silico assessment of catechins on the genes previously shown to be involved in Triple Negative Breast Cancer (TNBC) disease and also to evaluate the effects of catechin extracts from purple (TRFK306) and BB35 tea clones on the expression patterns of genes in the 4T1 TNBC cell line as mentioned above. Identification and quantification of different catechin contents in these two tea clones were performed by using High-Performance Liquid Chromatography (HPLC). In silico assessment including Absorption, Distribution, Metabolism and Excretion (ADME) study for drug-likeness evaluation, Drug target prediction, Protein-protein interaction (PPI) network analysis and construction, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment and analysis for targets and Molecular docking study was conducted. Simplified Molecular Input Line Entry System (SMILES) of 4 different tea catechins showed a target on 111 genes in TNBC and have high binding affinity to the receptor-binding pocket of chain A of Epidermal Growth Factor Receptor (EGFR) Extracellular Domains. Catechin extracts promoted the downregulation of expression of 6 genes (Egfr, App, Bcl2, Dnmt, Hif1a and Psmb5) and the upregulation of 2 genes (Casp3 and Gadd45b) in the 4T1 TNBC cell line. Catechin extracts from purple tea clones showed higher activity on gene expression levels of Egfr, Bcl2 and Casp3 than catechin extracts from BB35 tea clone.

Interpretable attention-based multi-encoder transformer based QSPR model for assessing toxicity and environmental impact of chemicals

The rising demand from consumer goods and pharmaceutical industry is driving a fast expansion of newly developed chemicals. The conventional toxicity testing of unknown chemicals is expensive, time-consuming, and raises ethical concerns. The quantitative structure–property relationship (QSPR) is an efficient computational method because it saves time, resources, and animal experimentation. Advances in machine learning have improved chemical analysis in QSPR studies, but the real-world application of machine learning-based QSPR studies was limited by the unexplainable ‘black box’ feature of the machine learnings. In this study, multi-encoder structure-to-toxicity (S2T)-transformer based QSPR model was developed to estimate the properties of polychlorinated biphenyls (PCBs) and endocrine disrupting chemicals (EDCs). Simplified molecular input line entry systems (SMILES) and molecular descriptors calculated by the Dragon 6 software, were simultaneously considered as input of QSPR model. Furthermore, an attention-based framework is proposed to describe the relationship between the molecular structure and toxicity of hazardous chemicals. The S2T-transformer model achieved the highest R2 scores of 0.918, 0.856, and 0.907 for logarithm of octanol-water partition coefficient (Log KOW), octanol-air partition coefficient (Log KOA), and bioconcentration factor (Log BCF) estimation of PCBs, respectively. Moreover, the attention weights were able to properly interpret the lateral (meta, para) chlorination associated with PCBs toxicity and environmental impact.

Screening of Phytochemicals for Antisickling effects

Background: Sickle cell disease is an autosomal recessive disorder caused due to point mutation on a single gene on chromosome 11 resulting in substitution of valine in place of glutamic acid at sixth position of beta-chain of adult hemoglobin (HbA). The purpose of the current study is to evaluate the effectiveness of phytochemicals (Rutin and its derivatives) for antisickling effects. Methods: The structure of Rutin is obtained in Simplified Molecular Input Line Entry System (SMILES) format and is subjected to chemical similarity search using Extended-connectivity fingerprints (ECFP4) fingerprints. The Tanimoto coefficient is used as the similarity metric and top compounds obtained on chemical similarity search are further subjected to molecular docking based screening analysis. A local database of phytochemicals is created using the IMPAAT database. Molecular docking approach is implemented using iGEMDOCK for screening and ranking of Rutin and its derivatives. The docking is implemented with standard docking protocol with population size of 200 and number of generations= 70. Results: All the ligand molecules showed acceptable binding energies with the 2HBS receptor molecule ranging between -124.87 to-79.59kcal/mol. Koprutin showed the highest binding affinity against the 2HBS (2HBS-DEOXYHEMOGLOBIN S) and which can be further developed as an antisickling agent. Conclusion: The integration of insilico modelling techniques with the wet lab based experimental methods can significantly reduce the cost in terms of time and other resources. Further the intricacies of molecular interactions taking place between ligands and receptors can be better understood with the help of molecular modelling techniques.

A multi-task learning model for predicting drugs combination synergy by analyzing drug–drug interactions and integrated multi-view graph data

This paper proposes a multi-task deep learning model for determining drug combination synergistic by simultaneously output synergy scores and synergy class labels. Initially, the two drugs are represented using a Simplified Molecular-Input Line-Entry (SMILE) system. Chemical structural features of the drugs are extracted from the SMILE using the RedKit package. Additionally, an improved Multi-view representation is proposed to extract graph-based drug features. Furthermore, the cancer cell line is represented by gene expression. Then, a three fully connected layers are learned to extract cancer cell line features. To investigate the impact of drug interactions on cell lines, the drug interaction features are extracted from a pretrained drugs interaction network and fed into an attention mechanism along with the cancer cell line features, resulting in the output of affected cancer cell line features. Subsequently, the drug and cell line features are concatenated and fed into an attention mechanism, which produces a two-feature representation for the two predicted tasks. The relationship between the two tasks is learned using the cross-stitch algorithm. Finally, each task feature is inputted into a fully connected subnetwork to predict the synergy score and synergy label. The proposed model ‘MutliSyn’ is evaluated using the O'Neil cancer dataset, comprising 38 unique drugs combined to form 22,737 drug combination pairs, tested on 39 cancer cell lines. For the synergy score, the model achieves a mean square error (MSE) of 219.14, a root mean square error (RMSE) of 14.75, and a Pearson score of 0.76. Regarding the synergy class label, the model achieves an area under the ROC curve (ROC-AUC) of 0.95, an area under the precision-recall curve (PR-AUC) of 0.85, precision of 0.93, kappa of 0.61, and accuracy of 0.90.

Initial development of web application to support excipient selection for immediate release tablets using message passing neural network

The formulation of immediate release tablets is a challenging task due to the need to balance multiple factors such as bioavailability and stability. The selection of appropriate excipients is critical in achieving these objectives. The recent successful application of Artificial Intelligence (AI) and Message Passing Neural Network (MPNN) in predicting physicochemical and biological properties of drug molecules suggests for the researchers on the ability to apply these models in selecting excipients. The aim of this study is to develop an innovative approach for selecting excipients using AI and MPNN and to create a user-friendly web application to support excipient selection for immediate release tablets. The study utilized a database of 13,278 immediate-release tablets to train, validate, and test the MPNN model on the basis of Simplified Molecular-Input Line-Entry system (SMILES) of drug substances. The performance of the model was validated based on its ability to predict the probability of selecting an excipient reasonably. A web application named FormAI was developed using the Streamlit web framework and integrated with the trained model. The MPNN model demonstrated good performance, with an average Area Under Curve > 0.98 and R2 > 0.99, indicating its ability to predict the probability of selecting an excipient reasonably. The FormAI application provides a user-friendly platform for excipient selection. The results of the study demonstrate the potential of using AI and MPNN in drug formulation design, specifically in excipient selection for immediate-release tablets. The FormAI application provides a practical solution for pharmaceutical scientists and formulators.

Computational drug discovery on human immunodeficiency virus with a customized long short-term memory variational autoencoder deep-learning architecture.

Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti-HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by HIV underscores the necessity for computational drug discovery methods to identify novel therapies. This investigation specifically focused on employing a long short-term memory (LSTM) variational autoencoder deep-learning architecture for computational drug discovery in relation to HIV. Our data set comprised simplified molecular input line entry system (SMILES)-encoded compounds, which were used to train the LSTM autoencoder. Remarkably, our model achieved a training accuracy of 91%, with a data set containing 1377 compounds. Leveraging the generative model derived from the training phase, we generated potential new drugs for combating HIV and assessed their interaction with the virus using a previously developed artificial intelligence model. Lastly, we verified the drug likeliness of our computationally generated compounds in accordance with Lipinski's rule of five. Overall, our study presents a promising approach to computational drug discovery in the ongoing battle against HIV.

Machine Learning-Assisted Discovery of Novel Anti-HIV Drug Candidates: An Analysis Using Molecular Datasets

Human Immunodeficiency Virus (HIV) serves as a crisis of global public health, necessitating new anti-HIV agents due to the virus's rapid mutation and subsequent drug resistance. While current Combination Antiretroviral Therapy (CART) has helped control the infection and mortality rates, traditional drug development approaches are costly and inefficient. This study aims to address this issue by applying machine learning algorithms for lead compound discovery using the extensive, quality-assured DTP Antiviral Screen Databases. Three molecular datasets—Extended-Connectivity Fingerprints (ECFP), Simplified Molecular-Input Line-Entry System (SMILES), and 2D molecular IMAGES—were processed using Principal Component Analysis (PCA), train-test splitting, and dataset balancing. Six machine learning algorithms were employed, including linear and nonlinear models, optimized through 5-fold cross-validation. The Area Under the Receiver Operating Characteristic (AUROC) curve was utilized to evaluate the models' performance, as well as the macro averaged precision, averaged recall, averaged F1 score, and balanced accuracy metrics. The ensemble models were constructed from the top-performing individual models. The best individual model, a SVM model trained on the ECFP dataset, achieved performance metrics of 0.78 on macro-averaged precision; 0.68 on macro-averaged recall; 0.72 on macro-averaged F1 score; 0.71 on balanced accuracy; and 0.75 on AUROC when evaluated on the testing data. The best ensemble model, fused with SVM, kNN, and logistic regression trained on the ECFP dataset, achieved performance metrics of 0.82 on macro-averaged precision; 0.67 on macro-averaged recall; 0.72 on macro-averaged F1 score; and 0.70 on balanced accuracy when evaluated on the testing data. The models were then applied to a Pubmed-extracted drug dataset, identifying several promising anti-HIV drug candidates, fulfilling the study's objective to improve the efficiency and success rate of new anti-HIV drug screening and discovery. In summary, this research demonstrates the transformative potential of machine learning in accelerating and optimizing the drug discovery process for HIV treatment.

Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19.

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information.

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.

EarlGAN: An enhanced actor–critic reinforcement learning agent-driven GAN for de novo drug design

Deep generative models, such as Generative Adversarial Networks (GANs), have attracted the attention of researchers in the denovo drug design field. However, traditional GANs are typically used for image processing. Therefore, they are unsuitable for Simplified Molecular-Input Line-Entry System (SMILES) strings due to their discrete nature. Previous studies addressed this problem by combining Reinforcement Learning (RL) approaches with Monte Carlo tree search. However, for large chemical datasets, the molecule generation process is time-consuming due to the lengthy atom-by-atom sampling process with cumulative reward, an essence of the Monte Carlo tree search-based RL approaches. To address this problem, we propose an enhanced actor–critic RL agent-driven GAN, called EarlGAN, for denovo drug design. Specifically, EarlGAN’s generator acts as an actor to generate SMILES strings, and the discriminator acts as a critic to perform discrimination. EarlGAN makes autoregressive predictions at the atomic level. While the generator is based on previously generated atoms, the discriminator discriminates using a bidirectional pass over the atoms, including the current atom that is being predicted. We integrate moment, global-level discrimination rewards, and information entropy maximization. The moment rewards reduce the computation time, and the global-level rewards ensure the consistency of the molecule, whereas the information entropy maximization leads to a more diverse sample generation. Experiments and ablation studies verify the effectiveness of EarlGAN for denovo drug design on the QM9 and ZINC datasets. In addition, the visualization analysis provides insight into EarlGAN and supports our conclusion.