Machine Learning Methods in Drug Discovery

First-in-human, double-blind, randomized phase 1b study of peptide immunotherapy IMCY-0098 in new-onset type 1 diabetes: an exploratory analysis of immune biomarkers

BACKGROUND

IMCY-0098, a synthetic peptide developed to halt disease progression via elimination of key immune cells in the autoimmune cascade, has shown a promising safety profile for the treatment of type 1 diabetes (T1D) in a recent phase 1b trial. This exploratory analysis of data from that trial aimed to identify the patient biomarkers at baseline associated with a positive response to treatment and examined the associations between immune response parameters and clinical efficacy endpoints (as surrogates for mechanism of action endpoints) using an artificial intelligence-based approach of unsupervised explainable machine learning.

METHODS

We conducted an exploratory analysis of data from a phase 1b, dose-escalation, randomized, placebo-controlled study of IMCY-0098 in patients with recent-onset T1D. Here, a panel of markers of T cell activation, memory T cells, and effector T cell response were analyzed via descriptive statistics. Artificial intelligence-based analyses of associations between all variables, including immune responses and clinical responses, were performed using the Knowledge Extraction and Management (KEM®) v 3.6.2 analytical platform.

RESULTS

The relationship between all available patient data was investigated using unsupervised machine learning implemented in the KEM® environment. Of 15 associations found for the dose C group (450 μg subcutaneously followed by 3 × 225 μg subcutaneously), seven involved human leukocyte antigen (HLA) type, all of which identified improvement/absence of worsening of disease parameters in DR4+ patients and worsening/absence of improvement in DR4- patients. This association with DR4+ and non-DR3 was confirmed using the endpoints normalized area under the curve C-peptide from mixed meal tolerance tests where presence of DR4 HLA haplotype was associated with an improvement in both endpoints. Exploratory immune analysis showed that IMCY-0098 dose B (150 μg subcutaneously followed by 3 × 75 μg subcutaneously) and dose C led to an increase in presumed/potentially protective antigen-specific cytolytic CD4+ T cells and a decrease in pathogenic CD8+ T cells, consistent with the expected mechanism of action of IMCY-0098. The analysis identified significant associations between immune and clinical responses to IMCY-0098.

CONCLUSIONS

Promising preliminary efficacy results support the design of a phase 2 study of IMCY-0098 in patients with recent-onset T1D.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03272269; EudraCT: 2016-003514-27.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

A comprehensive survey on the use of deep learning techniques in glioblastoma

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.

Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives

Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Cancer is considered one of the deadliest diseases across all age categories. Diagnosing such a deadly disease at the initial stage is essential to cure the disease. Hematologists and pathologists rely on microscopic evaluation of blood or bone marrow smear images to diagnose blood-related ailments. The abundance of overlapping cells, cells of varying densities among platelets, non-illumination levels, and the amount of red and white blood cells make it more difficult to diagnose illness using blood cell images. Pathologists are required to put more effort into the traditional, time-consuming system. Nowadays, it becomes possible with machine learning and deep learning techniques, to automate the diagnostic processes, categorize microscopic blood cells, and improve the accuracy of the procedure and its speed as the models developed using these methods may guide an assisting tool. In this article, we have acquired, analyzed, scrutinized, and finally selected around 57 research papers from various machine learning and deep learning methodologies that have been employed in the diagnosis of leukemia and its classification over the past 20 years, which have been published between the years 2003 and 2023 by PubMed, IEEE, Science Direct, Google Scholar and other pertinent sources. Our primary emphasis is on evaluating the advantages and limitations of analogous research endeavors to provide a concise and valuable research directive that can be of significant utility to fellow researchers in the field.

A narrative review of the application of machine learning in venous thromboembolism

OBJECTIVE

To summarize the current research progress of machine learning and venous thromboembolism.

METHODS

The literature on risk factors, diagnosis, prevention and prognosis of machine learning and venous thromboembolism in recent years was reviewed.

RESULTS

Machine learning is the future of biomedical research, personalized medicine, and computer-aided diagnosis, and will significantly promote the development of biomedical research and healthcare. However, many medical professionals are not familiar with it. In this review, we will introduce several commonly used machine learning algorithms in medicine, discuss the application of machine learning in venous thromboembolism, and reveal the challenges and opportunities of machine learning in medicine.

CONCLUSION

The incidence of venous thromboembolism is high, the diagnostic measures are diverse, and it is necessary to classify and treat machine learning, and machine learning as a research tool, it is more necessary to strengthen the special research of venous thromboembolism and machine learning.

Discovery of drug targets based on traditional Chinese medicine microspheres (TCM-MPs) fishing strategy combined with bio-layer interferometry (BLI) technology

Target discovery of natural products is a key step in the development of new drugs, and it is also a difficult speed-limiting step. In this study, a traditional Chinese medicine microspheres (TCM-MPs) target fishing strategy was developed to discover the key drug targets from complex system. The microspheres are composed of Fe3O4 magnetic nanolayer, oleic acid modified layer, the photoaffinity group (4- [3-(Trifluoromethyl)-3H-diazirin-3-yl] benzoic acid, TAD) layer and active small molecule layer from inside to outside. TAD produces highly reactive carbene under ultraviolet light, which can realize the self-assembly and fixation of drug active small molecules with non-selective properties. Here, taking Shenqi Jiangtang Granules (SJG) as an example, the constructed TCM-MPs was used to fish the related proteins of human glomerular mesangial cells (HMCs) lysate. 28 differential proteins were screened. According to the target analysis based on bioinformatics, GNAS was selected as the key target, which participated in insulin secretion and cAMP signaling pathway. To further verify the interaction effect of GNAS and small molecules, a reverse fishing technique was established based on bio-layer interferometry (BLI) coupled with UHPLC-Q/TOF-MS/MS. The results displayed that 26 small molecules may potentially interact with GNAS, and 7 of them were found to have strong binding activity. In vitro experiments for HMCs have shown that 7 active compounds can significantly activate the cAMP pathway by binding to GNAS. The developed TCM-MPs target fishing strategy combined with BLI reverse fishing technology to screen out key proteins that directly interact with active ingredients from complex target protein systems is significant for the discovery of drug targets for complex systems of TCM.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer

Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein-protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways.

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study

BACKGROUND

Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery.

METHODS

A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts.

RESULTS

Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, P <0.0001), 11.10 (95% CI: 5.467-22.520, P <0.0001), and 8.27 (95% CI: 1.482-46.130, P <0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC.

CONCLUSION

Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant.

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Building a Kokumi Database and Machine Learning-Based Prediction: A Systematic Computational Study on Kokumi Analysis

Kokumi is a subtle sensation characterized by a sense of fullness, continuity, and thickness. Traditional methods of taste discovery and analysis, including those of kokumi, have been labor-intensive and costly, thus necessitating the emergence of computational methods as critical strategies in molecular taste analysis and prediction. In this study, we undertook a comprehensive analysis, prediction, and screening of the kokumi compounds. We categorized 285 kokumi compounds from a previously unreleased kokumi database into five groups based on their molecular characteristics. Moreover, we predicted kokumi/non-kokumi and multi-flavor compositions using six structure-taste relationship models: MLP-E3FP, MLP-PLIF, MLP-RDKFP, SVM-RDKFP, RF-RDKFP, and WeaveGNN feature of Atoms and Bonds. These six predictors exhibited diverse performance levels across two different models. For kokumi/non-kokumi prediction, the WeaveGNN model showed an exceptional predictive AUC value (0.94), outperforming the other models (0.87, 0.90, 0.89, 0.92, and 0.78). For multi-flavor prediction, the MLP-E3FP model demonstrated a higher predictive AUC and MCC value (0.94 and 0.74) than the others (0.73 and 0.33; 0.92 and 0.70; 0.95 and 0.73; 0.94 and 0.64; and 0.88 and 0.69). This data highlights the model's proficiency in accurately predicting kokumi molecules. As a result, we sourced kokumi active compounds through a high-throughput screening of over 100 million molecules, further refined by toxicity and similarity screening. Lastly, we launched a web platform, KokumiPD (https://www.kokumipd.com/), offering a comprehensive kokumi database and online prediction services for users.

Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures

The human body consists of diverse subcellular, cellular and supracellular structures. Neurons possess varying-sized projections that interact with different cellular structures leading to the development of highly complex morphologies. Aiming to enhance image analysis of complex biological forms including neurons using available FIJI (ImageJ) plugins, Lusca, an advanced open-source tool, was developed. Lusca utilizes machine learning for image segmentation with intensity and size thresholds. It performs particle analysis to ascertain parameters such as area/volume, quantity, and intensity, in addition to skeletonization for determining length, branching, and width. Moreover, in conjunction with colocalization measurements, it provides an extensive set of 29 morphometric parameters for both 2D and 3D analysis. This is a significant enhancement compared to other scripts that offer only 5-15 parameters. Consequently, it ensures quicker and more precise quantification by effectively eliminating noise and discerning subtle details. With three times larger execution speed, fewer false positive and negative results, and the capacity to measure various parameters, Lusca surpasses other existing open-source solutions. Its implementation of machine learning-based segmentation facilitates versatile applications for different cell types and biological structures, including mitochondria, fibres, and vessels. Lusca's automated and precise measurement capability makes it an ideal choice for diverse biological image analyses.

Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

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.

Influence of Bile Acids on Clindamycin Hydrochloride Skin Permeability: In Vitro and In Silico Preliminary Study

BACKGROUND AND OBJECTIVE

Topical clindamycin formulations are widely used in clinical practice, but poor bioavailability and restricted skin penetration considerably limit their therapeutic efficacy. Penetration enhancement represents a promising and rational strategy to overcome the drawbacks of conventional topical pharmaceutical formulations. We aim to assess the influence of cholic acid (CA) and deoxycholic acid (DCA) on the permeability of clindamycin hydrochloride by performing the in vitro skin parallel artificial membrane permeability assay (skin-PAMPA) at two relevant pH values (5.5 and 6.5) and the interactions of tested substances with skin ATP-binding cassette (ABC) transporters in silico.

METHODS

After the incubation period, the clindamycin hydrochloride concentrations in both compartments were determined spectrophotometrically, and the apparent permeability coefficients (Papp) were calculated. Vienna LiverTox web service was used to predict the interactions of clindamycin and bile acids with potential drug transporters located in human skin.

RESULTS

Both CA and DCA at the highest studied concentration of 100 μM in the tested solutions increased the skin-PAMPA membrane permeability of clindamycin hydrochloride. This effect was more pronounced for CA and at a higher studied pH value of 6.5, which is characteristic of most dermatological indications treated with topical clindamycin preparations. Clindamycin transport may also be mediated by ABC transporters located in skin and facilitated in the presence of bile acids.

CONCLUSIONS

The results of this study provide a solid foundation for further research directed at the improvement of topical formulations using bile acids as penetration-enhancing excipients, as well as the therapeutic efficacy of clindamycin hydrochloride.

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

INTRODUCTION

Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.

OBJECTIVE

The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.

METHODS

A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.

RESULTS

Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).

CONCLUSION

To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.

LEVEL OF EVIDENCE

5 Laryngoscope, 134:1333-1339, 2024.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Discovery of new antiviral agents through artificial intelligence: In vitro and in vivo results

In this research, we employed a deep reinforcement learning (RL)-based molecule design platform to generate a diverse set of compounds targeting the neuraminidase (NA) of influenza A and B viruses. A total of 60,291 compounds were generated, of which 86.5 % displayed superior physicochemical properties compared to oseltamivir. After narrowing down the selection through computational filters, nine compounds with non-sialic acid-like structures were selected for in vitro experiments. We identified two compounds, DS-22-inf-009 and DS-22-inf-021 that effectively inhibited the NAs of both influenza A and B viruses (IAV and IBV), including H275Y mutant strains at low micromolar concentrations. Molecular dynamics simulations revealed a similar pattern of interaction with amino acid residues as oseltamivir. In cell-based assays, DS-22-inf-009 and DS-22-inf-021 inhibited IAV and IBV in a dose-dependent manner with EC50 values ranging from 0.29 μM to 2.31 μM. Furthermore, animal experiments showed that both DS-22-inf-009 and DS-22-inf-021 exerted antiviral activity in mice, conferring 65 % and 85 % protection from IAV (H1N1 pdm09), and 65 % and 100 % protection from IBV (Yamagata lineage), respectively. Thus, these findings demonstrate the potential of RL to generate compounds with promising antiviral properties.

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol.

Paradigms and Success Stories of Natural Products in Drug Discovery Against Neurodegenerative Disorders (NDDs)

Neurodegenerative disorders (NDDs) are multifaceted complex disorders that have put a great health and economic burden around the globe nowadays. The multi-factorial nature of NDDs has presented a great challenge in drug discovery and continuous efforts are in progress in search of suitable therapeutic candidates. Nature has a great wealth of active principles in its lap that has cured the human population since ancient times. Natural products have revealed several benefits over conventional synthetic medications and scientists have shifted their vision towards exploring the therapeutic potentials of natural products in the past few years. The structural mimicking of natural compounds to endogenous ligands has presented them as a potential therapeutic candidate to prevent the development of NDDs. In the presented review, authors have summarized demographical facts about various NDDs including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and various types of sclerosis in the brain. The significant findings of new active principles of natural origin along with their therapeutic potentials on NDDs have been included. Also, a description of clinical trials and patents on natural products has been enlisted in this compilation. Although natural products have shown promising success in drug discovery against NDDs, still their use is associated with several ethical issues which need to be solved in the upcoming time.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.

Prediction of resveratrol target proteins: a bioinformatics analysis

Resveratrol is a natural compound with a wide range of biological functions that generate health benefits under normal conditions and in multiple diseases. This has attracted the attention of the scientific community, which has revealed that this compound exerts these effects through its action on different proteins. Despite the great efforts made, due to the challenges involved, not all the proteins with which resveratrol interacts have yet been identified. In this work, using protein target prediction bioinformatics systems, RNA sequencing analysis and protein-protein interaction networks, 16 proteins were identified as potential targets of resveratrol. Due to its biological relevance, the interaction of resveratrol with the predicted target CDK5 was further investigated. A docking analysis found that resveratrol can interact with CDK5 and be positioned in its ATP-binding pocket. Resveratrol forms hydrogen bonds between its three hydroxyl groups (-OH) and CDK5 residues C83, D86, K89 and D144. Molecular dynamics analysis showed that these bonds allow resveratrol to remain in the pocket and suggest inhibition of CDK5 activity. All this allows us to better understand how resveratrol acts and to consider CDK5 inhibition within its biological actions, mainly in neurodegenerative diseases where this protein has been shown to be relevant.Communicated by Ramaswamy H. Sarma.

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.

Conjoint research of WGCNA, single-cell transcriptome and structural biology reveals the potential targets of IDD development and treatment and JAK3 involvement

OBJECTIVES

This study conducted integrated analysis of bulk RNA sequencing, single-cell RNA sequencing and Weighted Gene Co-expression Network Analysis (WGCNA), to comprehensively decode the most essential genes of intervertebral disc degeneration (IDD); then mainly focused on the JAK3 macromolecule to identify natural compounds to provide more candidate drug options in alleviating IDD.

METHODS

In the first part, we performed single-cell transcriptome analysis and WGCNA workflow to delineate the most pivotal genes of IDD. Then series of structural biology approaches and high-throughput virtual screening techniques were performed to discover potential compounds targeting JAK-STAT signaling pathway, such as Libdock, ADMET, precise molecular docking algorithm and in-vivo drug stability assessment.

RESULTS

Totally 4 hub genes were determined in the development of IDD, namely VEGFA, MMP3, TNFSF11, and TIMP3, respectively. Then, 3 novel natural materials, ZINC000014952116, ZINC000003938642 and ZINC000072131515, were determined as potential compounds, with less toxicities and moderate ADME characteristics. In-vivo drug stability assessment suggested that these drugs could interact with JAK3, and their ligand-JAK3 complexes maintained the homeostasis in-vivo, which acted as regulatory role to JAK3 protein. Among them, ZINC000072131515, also known as Menaquinone, demonstrated significant protective roles to alleviate the progression of IDD in vitro, which proved the nutritional therapy in alleviating IDD.

CONCLUSIONS

This study reported the essential genes in the development of IDD, and also the roles of Menaquinone to ameliorate IDD through inhibiting JAK3 protein. This study also provided more options and resources on JAK3 targeted screening, which may further expand the drug resources in the pharmaceutical market.

Use of tree-based machine learning methods to screen affinitive peptides based on docking data

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy

Epilepsy, a prevalent chronic disorder of the central nervous system, is typified by recurrent seizures. Present treatments predominantly offer symptomatic relief by managing seizures, yet fall short of influencing epileptogenesis. This study endeavored to identify novel phytochemicals with potential therapeutic efficacy against S100B, an influential protein in epileptogenesis, through an innovative application of machine learning-enabled virtual screening. Our study incorporated the use of multiple machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), and Random Forest (RF). These algorithms were employed not only for virtual screening but also for essential feature extraction and selection, enhancing our ability to distinguish between active and inactive compounds. Among the tested machine learning algorithms, the RF model outshone the rest, delivering an impressive 93.43 % accuracy on both training and test datasets. This robust RF model was leveraged to sift through the library of 9,000 phytochemicals, culminating in the identification of 180 potential inhibitors of S100B. These 180 active compounds were than docked with the active site of S100B proteins. The results of our study highlighted that the 6-(3,12-dihydroxy-4,10,13-trimethyl-7,11-dioxo-2,3,4,5,6,12,14,15,16,17-decahydro-1H cyclopenta[a] phenanthren -17-yl)-2-methyl-3-methylideneheptanoic acid, rhinacanthin K, thiobinupharidine, scopadulcic acid, and maslinic acid form significant interactions within the binding pocket of S100B, resulting in stable complexes. This underscores their potential role as S100B antagonists, thereby presenting novel therapeutic possibilities for epilepsy management. To sum up, this study's deployment of machine learning in conjunction with virtual screening not only has the potential to unearth new epilepsy therapeutics but also underscores the transformative potential of these advanced computational techniques in streamlining and enhancing drug discovery processes.

Regulations of malaria in children with human immunodeficiency virus infection: A review

This comprehensive review explores the intricate relationship between 2 major global health challenges, malaria and HIV, with a specific focus on their impact on children. These diseases, both endemic in sub-Saharan Africa, create a dual burden that significantly elevates the risk of morbidity and mortality, particularly in children with compromised immune systems due to HIV. The review delves into the complex mechanisms by which these infections interact, from heightened clinical malaria frequencies in HIV-infected individuals to the potential impact of antiretroviral therapy on malaria treatment. Different research engines were utilized in writing this paper such as Web of Science, Google Scholar, Pubmed Central, ResearchGate, and Academia Edu. To address this critical health concern, the study identifies and discusses various regulatory and treatment strategies. It emphasizes the importance of daily cotrimoxazole prophylaxis and insecticide-treated nets in preventing malaria in children with HIV. The potential of antiretroviral protease inhibitors and mRNA-based vaccines as innovative solutions is highlighted. Additionally, the study underscores the significance of climate data and artificial intelligence in improving diagnostics and drug development. Furthermore, the review introduces the concept of genetically modified mosquitoes as a novel approach to vector control, offering a promising avenue to protect HIV-positive individuals from mosquito-borne diseases like malaria. Through a comprehensive analysis of these strategies, the study aims to provide a foundation for policymakers, healthcare professionals, and researchers to develop effective regulations and interventions that reduce the dual burden of malaria and HIV in children, improving public health outcomes in endemic regions.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation

Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into effective antidiabetic designs. Therefore, in this study, we used machine learning-based small molecule design. We used various chemoinformatic and binary fingerprint techniques on small molecules to construct multiple models for alpha-amylase inhibitors. Among these models, the top models were used for ensemble-based machine learning predictions on libraries of organic molecules supplemented with synthetic scaffolds that could be used as antidiabetic agents. Further, involved identifying 10 promising molecules from computational studies and determining their inhibitory effects on alpha-amylase. These molecules were synthesised and thoroughly analysed to assess their biological inhibitory properties. Then, thermodynamic simulations were conducted to determine the stability and affinity of experimentally active molecules. The research results showcased the top 10 ML models recorded impressive statistics with an average model score of 0.8216, Pearson-r value of 0.827 and external validation yielding a Q2 value of 0.835, proving their reliability and accuracy. Ten derivatives of benzothiophene dioxolane was prime research focus due to computational predictions. The biological inhibitory assay of synthesised molecules showed that small molecules with ID ALC5 and ALC6 exhibited inhibitory efficiencies (IC50) of 2.1 ± 0.14 µM and 5.71 ± 0.02 µM against alpha-amylase enzyme, whereas other molecules showed moderate inhibition. In conclusion, the positive results of the experiment indicate that researchers should explore machine learning-driven design.Communicated by Ramaswamy H. Sarma.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

In silico prediction of hERG blockers using machine learning and deep learning approaches

The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking property of compounds in early drug discovery. In this study, a dataset containing 4556 compounds with IC50 values determined by patch clamp techniques on mammalian lineage cells was collected, and hERG blockers and non-blockers were distinguished according to three single thresholds and two binary thresholds. Four machine learning (ML) algorithms combining four molecular fingerprints and molecular descriptors as well as graph convolutional neural networks (GCNs) were used to construct a series of binary classification models. The results showed that the best models varied for different thresholds. The ML models implemented by support vector machine and random forest performed well based on Morgan fingerprints and molecular descriptors, with AUCs ranging from 0.884 to 0.950. GCN showed superior prediction performance with AUCs above 0.952, which might be related to its direct extraction of molecular features from the original input. Meanwhile, the classification of binary threshold was better than that of single threshold, which could provide us with a more accurate prediction of hERG blockers. At last, the applicability domain for the model was defined, and seven structural alerts that might generate hERG blockage were identified by information gain and substructure frequency analysis. Our work would be beneficial for identifying hERG blockers in chemicals.

Meta-learning for transformer-based prediction of potent compounds

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.

Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models

Machine learning (ML) approaches have been applied to predicting drug pharmacokinetic properties. Previously, we predicted rat unbound brain-to-plasma ratio (Kpuu,brain) by ML models. In this study, we aimed to predict human Kpuu,brain through animal ML models. First, we re-evaluated ML models for rat Kpuu,brain prediction by using trendy open-source packages. We then developed ML models for monkey Kpuu,brain prediction. Leave-one-out cross validation was utilized to rationally build models using a relatively small dataset. After establishing the monkey and rat ML models, human Kpuu,brain prediction was achieved by implementing the animal models considering appropriate scaling methods. Mechanistic NeuroPK models for the identical monkey and human dataset were treated as the criteria for comparison. Results showed that rat Kpuu,brain predictivity was successfully replicated. The optimal ML model for monkey Kpuu,brain prediction was superior to the NeuroPK model, where accuracy within 2-fold error was 78% (R2 = 0.76). For human Kpuu,brain prediction, rat model using relative expression factor (REF), scaled transporter efflux ratios (ERs), and monkey model using in vitro ERs can provide comparable predictivity to the NeuroPK model, where accuracy within 2-fold error was 71% and 64% (R2 = 0.30 and 0.52), respectively. We demonstrated that ML models can deliver promising Kpuu,brain prediction with several advantages: (1) predict reasonable animal Kpuu,brain; (2) prospectively predict human Kpuu,brain from animal models; and (3) can skip expensive monkey studies for human prediction by using the rat model. As a result, ML models can be a powerful tool for drug Kpuu,brain prediction in the discovery stage.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Closed-Loop Medication Management with an Electronic Health Record System in U.S. and Finnish Hospitals

Many medication errors in the hospital setting are due to manual, error-prone processes in the medication management system. Closed-loop Electronic Medication Management Systems (EMMSs) use technology to prevent medication errors by replacing manual steps with automated, electronic ones. As Finnish Helsinki University Hospital (HUS) establishes its first closed-loop EMMS with the new Epic-based Electronic Health Record system (APOTTI), it is helpful to consider the history of a more mature system: that of the United States. The U.S. approach evolved over time under unique policy, economic, and legal circumstances. Closed-loop EMMSs have arrived in many U.S. hospital locations, with myriad market-by-market manifestations typical of the U.S. healthcare system. This review describes and compares U.S. and Finnish hospitals' EMMS approaches and their impact on medication workflows and safety. Specifically, commonalities and nuanced differences in closed-loop EMMSs are explored from the perspectives of the care/nursing unit and hospital pharmacy operations perspectives. As the technologies are now fully implemented and destined for evolution in both countries, perhaps closed-loop EMMSs can be a topic of continued collaboration between the two countries. This review can also be used for benchmarking in other countries developing closed-loop EMMSs.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

A Rational Approach to Predicting Immediate Release Formulation Behavior in Multiple Gastric Motility Patterns: A Combination of a Biorelevant Apparatus, Design of Experiments, and Machine Learning

Gastric mechanical stress often impacts drug dissolution from solid oral dosage forms, but in vitro experiments cannot recreate the substantial variability of gastric motility in a reasonable time. This study, for the first time, combines a novel dissolution apparatus with the design of experiments (DoE) and machine learning (ML) to overcome this obstacle. The workflow involves the testing of soft gelatin capsules in a set of fasted-state biorelevant dissolution experiments created with DoE. The dissolution results are used by an ML algorithm to build the classification model of the capsule's opening in response to intragastric stress (IS) within the physiological space of timing and magnitude. Next, a random forest algorithm is used to model the further drug dissolution. The predictive power of the two ML models is verified with independent dissolution tests, and they outperform a polynomial-based DoE model. Moreover, the developed tool reasonably simulates over 50 dissolution profiles under varying IS conditions. Hence, we prove that our method can be utilized for the simulation of dissolution profiles related to the multiplicity of individual gastric motility patterns. In perspective, the developed workflow can improve virtual bioequivalence trials and the patient-centric development of immediate-release oral dosage forms.

A Random Forest Model for Peptide Classification Based on Virtual Docking Data

The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

Hybrid neural network approaches to predict drug-target binding affinity for drug repurposing: screening for potential leads for Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug-target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein-protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA.

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

BACKGROUND

Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications.

OBJECTIVE

The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures.

METHODS

We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported.

RESULTS

The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings.

CONCLUSIONS

We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research.

TRIAL REGISTRATION

PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/45823.

Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence

This article provides a comprehensive narrative review of the history of antiepileptic drugs (AEDs) and their development over time. Firstly, it explores the significant role of serendipity in the discovery of essential AEDs that continue to be used today, such as phenobarbital and valproic acid. Subsequently, it delves into the historical progression of crucial preclinical models employed in the development of novel AEDs, including the maximal electroshock stimulation test, pentylenetetrazol-induced test, kindling models, and other animal models. Moving forward, a concise overview of the clinical advancement of major AEDs is provided, highlighting the initial milestones and the subsequent refinement of this process in recent decades, in line with the emergence of evidence-based medicine and the implementation of increasingly rigorous controlled clinical trials. Lastly, the article explores the contributions of artificial intelligence, while also offering recommendations and discussing future perspectives for the development of new AEDs.

Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics

With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.

X-ray crystallography and sickle cell disease drug discovery-a tribute to Donald Abraham

X-ray crystallography and structure-based drug discovery have played a major role in the discovery of antisickling agents that target hemoglobin (Hb) for the treatment of sickle cell disease (SCD). Sickle cell disease, the most common inherited hematologic disorder, occurs as a result of a single point mutation of βGlu6 in normal human adult hemoglobin (HbA) to βVal6 in sickle hemoglobin (HbS). The disease is characterized by polymerization of HbS and sickling of red blood cells (RBCs), leading to several secondary pathophysiologies, including but not limited to vaso-occlusion, hemolytic anemia, oxidative stress, inflammation, stroke, pain crisis, and organ damage. Despite the fact that SCD was the first disease to have its molecular basis established, the development of therapies was for a very long time a challenge and took several decades to find therapeutic agents. The determination of the crystal structure of Hb by Max Perutz in the early 60s, and the pioneering X-ray crystallography research by Donald J. Abraham in the early 80s, which resulted in the first structures of Hb in complex with small molecule allosteric effectors of Hb, gave much hope that structure-based drug discovery (SBDD) could be used to accelerate development of antisickling drugs that target the primary pathophysiology of hypoxia-induced HbS polymerization to treat SCD. This article, which is dedicated to Donald J. Abraham, briefly reviews structural biology, X-ray crystallography and structure-based drug discovery from the perspective of Hb. The review also presents the impact of X-ray crystallography in SCD drug development using Hb as a target, emphasizing the major and important contributions by Don Abraham in this field.

Drug repurposing and prediction of multiple interaction types via graph embedding

BACKGROUND

Finding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug-target links, as well as delineating the type of drug interaction, are important in drug repurposing studies.

RESULTS

A computational drug repurposing approach was proposed to predict novel drug-target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug-drug and protein-protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers.

CONCLUSION

DT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug-target-disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.

Consensus Ensemble Multitarget Neural Network Model of Anxiolytic Activity of Chemical Compounds and Its Use for Multitarget Pharmacophore Design

A classification consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready been tested for anxiolytic activity and were structurally similar to the 15 studied nitrogen-containing heterocyclic chemotypes. Seventeen biotargets relevant to anxiolytic activity were selected, taking into account the possible effect on them of the derivatives of these chemotypes. The generated model consistedof three ensembles of artificial neural networks for predicting three levels of anxiolytic activity, with sevenneural networks in each ensemble. A sensitive analysis of neurons in an ensemble of neural networks for a high level of activity made it possible to identify four biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut, which were the most significant for the manifestation of the anxiolytic effect. For these four key biotargets for 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives, eight monotarget pharmacophores of high anxiolytic activity were built. Superposition of monotarget pharmacophores built two multitarget pharmacophores of high anxiolytic activity, reflecting the universal features of interaction 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives with the most significant biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut.