Drug Discovery Domain Research Articles

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology and demonstrate its applicability to targeted molecular generation. When applied to c-Abl kinase, a protein with FDA-approved small-molecule inhibitors, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. To facilitate implementation and reproducibility, we made all of our software available through the open-source ChemSpaceAL Python package.

Metaverse Applications in Bioinformatics: A Machine Learning Framework for the Discrimination of Anti-Cancer Peptides

Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immersive patient care experiences. These advanced technologies, coupled with the power of bioinformatics and genomic data, facilitate groundbreaking developments. The precise prediction of ACPs from complex biological sequences remains an ongoing challenge in the genomic area. Currently, conventional approaches such as chemotherapy, target therapy, radiotherapy, and surgery are widely used for cancer treatment. However, these methods fail to completely eradicate neoplastic cells or cancer stem cells and damage healthy tissues, resulting in morbidity and even mortality. To control such diseases, oncologists and drug designers highly desire to develop new preventive techniques with more efficiency and minor side effects. Therefore, this research provides an optimized computational-based framework for discriminating against ACPs. In addition, the proposed approach intelligently integrates four peptide encoding methods, namely amino acid occurrence analysis (AAOA), dipeptide occurrence analysis (DOA), tripeptide occurrence analysis (TOA), and enhanced pseudo amino acid composition (EPseAAC). To overcome the issue of bias and reduce true error, the synthetic minority oversampling technique (SMOTE) is applied to balance the samples against each class. The empirical results over two datasets, where the accuracy of the proposed model on the benchmark dataset is 97.56% and on the independent dataset is 95.00%, verify the effectiveness of our ensemble learning mechanism and show remarkable performance when compared with state-of-the-art (SOTA) methods. In addition, the application of metaverse technology in healthcare holds promise for transformative innovations, potentially enhancing patient experiences and providing novel solutions in the realm of preventive techniques and patient care.

Advances in machine intelligence-driven virtual screening approaches for big-data.

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures.

The field of bioimage analysis is currently impacted by a profound transformation, driven by the advancements in imaging technologies and artificial intelligence. The emergence of multi-modal AI systems could allow extracting and utilizing knowledge from bioimaging databases based on information from other data modalities. We leverage the multi-modal contrastive learning paradigm, which enables the embedding of both bioimages and chemical structures into a unified space by means of bioimage and molecular structure encoders. This common embedding space unlocks the possibility of querying bioimaging databases with chemical structures that induce different phenotypic effects. Concretely, in this work we show that a retrieval system based on multi-modal contrastive learning is capable of identifying the correct bioimage corresponding to a given chemical structure from a database of ~2000 candidate images with a top-1 accuracy >70 times higher than a random baseline. Additionally, the bioimage encoder demonstrates remarkable transferability to various further prediction tasks within the domain of drug discovery, such as activity prediction, molecule classification, and mechanism of action identification. Thus, our approach not only addresses the current limitations of bioimaging databases but also paves the way towards foundation models for microscopy images.

An interpretable graph representation learning model for accurate predictions of drugs aqueous solubility

As increasingly more data science-driven approaches have been applied for compound properties predictions in the domain of drug discovery, such kinds of methods have displayed considerable accuracy compared to conventional ones. In this work, we proposed an interpretable graph learning representation model, SolubNet, for drug aqueous solubility prediction. The comprehensive evaluation demonstrated that SolubNet can successfully capture the quantitative structure-property relationship and can be interpreted with layer-wise relevance propagation (LRP) algorithm regarding how prediction values are generated from original input structures. The key advantage of SolubNet lies in the fact that it includes 3 layers of Topology Adaptive Graph Convolutional Networks which can efficiently perceive chemical local environments. SolubNet showed high performance in several tasks for drugs’ aqueous solubility prediction. LRP revealed that SolubNet can identify high and low polar regions of a given molecule, assigning them reasonable weights to predict the final solubility, in a way highly compatible with chemists’ intuition. We are confident that such a flexible yet interpretable and accurate tool will largely enhance the efficiency of drug discovery, and will even contribute to the methodology development of computational pharmaceutics.

Nanopolyphenols

Recently nanopolyphenols are gaining widespread interest in the drug discovery domain. Nanonization of polyphenols has greatly affected the therapeutic index owing to improvement in pharmacokinetic and biopharmaceutical obstacles linked with the use of natural polyphenols. They have been looking at an emerging paradigm for an array of disease symptoms. In this article, we have explored the therapeutic potential of nanopolyphenols in oxidative stress-induced diseases such as neurodegeneration, cancer, obesity, and diabetes. This article will present the current state of the art of various nanopolyphenols targeting oxidative stress-induced diseases. The advanced fabrication strategies presented for polyphenols including nanocrystal, mesoporous silica nanoparticles, nanoparticles, nanoliposome, gold nanoparticle, and nanosuspension are discussed. The information presented in light of recent in vitro, in vivo, and clinical evidence for nanoformulation and delivery of polyphenols may show a new dimension to future research in the realm of herbal therapy for oxidative stress-induced diseases. Significant information on the molecular mechanisms underlying linkages of oxidative stress with neurodegenerative diseases, cancer, obesity, and diabetes is discussed. Valuable information on dietary polyphenols in these diseases and their clinical data is presented. Based on different experimental evidence, the review findings support phenomenal therapeutic strategies for nanopolyphenolic fabrication with extended benefits and a condensed time frame. The status of clinical trials conducted on nanopolyphenols is presented. Although clinical trials conducted on nanopolyphenols for mentioned diseases are few, we have tried to present as much available clinical data in this article.

SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions.

Capturing comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, graph neural networks (GNNs) have received increasing attention in the drug discovery domain due to their capability of integrating drugs profiles and the network structure into a low-dimensional feature space for predicting links and classification. Most of GNN models for DDI predictions are built on an unsigned graph, which tends to represent associated nodes with similar embedding results. However, semantic correlation between drugs, such as degressive effects, or even adverse side reactions should be disassortative. In this study, we put forward signed GNNs to model assortative and disassortative relationships within drug pairs. Since negative links exclude direct generalization of spectral filters on unsigned graph, we divide the signed graph into two unsigned subgraphs to dedicate two spectral filters, which captures both commonality and difference of drug pairs. For drug representations we derive two signed graph filtering-based neural networks (SGFNNs) which integrate signed graph structures and drug node attributes. Moreover, we use an end-to-end framework for learning DDIs, where an SGFNN together with a discriminator is jointly trained under a problem-specific loss function. The experimental results on two prediction problems show that our framework can obtain significant improvements compared with baselines. The case study further verifies the validation of our method.

Understanding the performance of knowledge graph embeddings in drug discovery

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab-based experiments being performed, or impact on other decisions, incurring significant time and financial costs and most importantly, ultimately influencing patient healthcare. For KGE models to have impact in this domain, a better understanding of not only of performance, but also the various factors which determine it, is required.In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our goal is not to focus on the best overall model or configuration, instead we take a deeper look at how performance can be affected by changes in the training setup, choice of hyperparameters, model parameter initialisation seed and different splits of the datasets. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models. Indeed these factors should be reported along with model architectures to ensure complete reproducibility and fair comparisons of future work, and we argue this is critical for the acceptance of use, and impact of KGEs in a biomedical setting.

COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

Manifold medicine: A schema that expands treatment dimensionality

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called ‘Manifold Medicine’, in which disease states are described by vectorial positions on several body-wide axes. Thus, pathological states are represented by multidimensional ‘vectors’ that traverse the body-wide axes. We then delineate the manifold nature of drug action to provide a strategy for designing manifold drug cocktails by design using state-of-the-art biomedical and technological innovations. Manifold Medicine offers a roadmap for translating knowledge gained from next-generation technologies into individualized clinical practice.

Feature Based Method for Predicting Pharmacological Interaction

Prediction of drug target interaction is an extrusive domain of drug discovery and repositioning of drugs. Most conventional studies are carried out in early years in the wet laboratory, but it is very expensive and time consuming. So nowadays, the use of machine learning techniques to predict drug target pairs. A new method of interaction targeting drugs is introduced in this paper. Use the Pseudo Position Specific Scoring Matrix (PsePSSM) is used to represent the target, which generate features that describe the original information of protein. The drug chemical structure information can be extracted through FP2 molecular fingerprint which describe the molecular structure information. Then a drug target interaction network is constructed using bipartite graph where in which each node represents a target or drug and each link indicates a drug target interaction. From the above stages, the data contains some noise and redundant data which have a negative impact on the prediction output. So, LASSO (Least Absolute Shrinkage and Selection Operator) method is handle it and reduce the dimension of the extracted feature information of original data. But drug target pair samples have some imbalanced, then cost-sensitive ensemble method is used to address the imbalanced problem between positive and negative samples, and learns about the minority class by assigning higher costs and optimizing their cost error. Finally, the processed data is given as input to the extreme gradient boosting classifier algorithm for predicting new drug target interaction pairs. This method can significantly improve the prediction accuracy of drug target interaction.

Teixobactin: A Paving Stone toward a New Class of Antibiotics?

Antimicrobial resistance is a serious threat to human health worldwide, prompting research efforts on a massive scale in search of novel antibiotics to fill an urgent need for a remedy. Teixobactin, a macrocyclic depsipeptide natural product, isolated from uncultured bacteria (Eleftheria terrae), displayed potent activity against several Gram-positive pathogenic bacteria. The distinct pharmacological profile and interesting structural features of teixobactin with nonstandard amino acid (three d-amino acids and l-allo-enduracididine) residues attracted several research groups to work on this target molecule in search of novel antibiotics with new mechanism. Herein, we present a comprehensive and critical perspective on immense possibilities offered by teixobactin in the domain of drug discovery. Efforts made by various research groups since its isolation are discussed, highlighting the molecule's considerable potential with special emphasis on replacement of amino acids. Critical analysis of synthetic efforts, SAR studies, and the way forward are provided hereunder.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

Molecular image-based convolutional neural network for the prediction of ADMET properties

Convolutional neural network (CNN), is one of the most representative architectures in deep learning and is widely adopted in many fields especially in image classification and object detection. In the last few years, CNN has been aroused more and more attentions in drug discovery domain. In this work, molecular 2-D image-based CNN method was used to establish prediction models of the ADMET properties, including CYP1A2 inhibitory potency, P-glycoprotein (P-gp) inhibitory activity, Blood-Brain Barrier (BBB) penetrating activity, and Ames mutagenicity. The results showed that the predictive power of the established CNN models is comparable to that of the available machine learning models based on manual structural description and feature selection. It can be inferred that CNN can extract efficiently the key image features related to the molecular ADMET properties and offer a useful tool for virtual screening and drug design researches.

Future challenges with DNA-encoded chemical libraries in the drug discovery domain

ABSTRACTIntroduction: DNA-encoded chemical libraries (DELs) have come of age and emerged to become a powerful technology platform for ligand discovery in biomedical research and drug discovery. Today, DELs have been widely adopted in the pharmaceutical industry and employed in drug discovery programs worldwide. DELs are capable of interrogating drug targets with an extremely large number of compounds highly efficiently.Area covered: In this review, the authors introduce the history of DELs and provide an overview of the major technological components, including encoding methods, library synthesis, chemistry, selection methods, hit deconvolution strategy, and post-selection data analysis. A brief update on the hit compounds recently discovered from DEL selections against drug targets is also provided. Finally, the authors discuss their views on the present challenges and future directions for the development and application of DELs in drug discovery.Expert opinion: DELs have provided great opportunities for lead compound discovery at an unprecedented scale and efficiency in drug discovery. The key to the future success of DELs as true discovery modalities, rather than just ‘a way to make many compounds,’ is to go beyond physical binding to functional or even phenotypic assays with the capability to probe the biological system.

How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM).

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.

Recent Advances in Heterogeneous Parallel Processing Schemes for Protein- Ligand Docking

Objectives: Molecular docking is widely used for molecular level recognition of leads and compounds which might be useful in the drug discovery domain. Docking is done in order to predict the binding mode and binding affinity of a complex which is formed by two or more constituent molecules with known structure. Objective is to find out the best available techniques in the docking domain. Methodology: We use Hex tool to simulate the various parameters and find out the bottlenecks. Fast Fourier Transform (FFT) and Spherical Polar Transformations (SPT) are applied to study the docking process. Findings: Various performance bottlenecks and parameter effect on the simulation. Improvements: It is observed that GPU optimization is possible by using FFT and SPT. The ligand space considered was limited to shape complementary. This could be considered for future e work.

Visible-Light, Iodine-Promoted Formation of N-Sulfonyl Imines and N-Alkylsulfonamides from Aldehydes and Hypervalent Iodine Reagents.

Alternative synthetic methodology for the direct installation of sulfonamide functionality is a highly desirable goal within the domain of drug discovery and development. The formation of synthetically valuable N-sulfonyl imines from a range of aldehydes, sulfonamides, and PhI(OAc)2 under practical and mild reaction conditions has been developed. According to mechanistic studies described within, the reaction proceeds through an initial step involving a radical initiator (generated either by visible-light or heat) to activate the reacting substrates. The reaction provides a synthetically useful and operationally simple, relatively mild alternative to the traditional formation of N-sulfonyl imines that utilizes stable, widely available reagents.

Convolutional neural network scoring and minimization in the D3R 2017 community challenge.

We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligand poses near and far from the true binding mode when given a set of reference receptors and classifying ligands as active or inactive using structural information. We use the CNN to re-score or refine poses generated using a conventional scoring function, Autodock Vina, and compare the performance of each of these methods to using the conventional scoring function alone. Furthermore, we assess several ways of choosing appropriate reference receptors in the context of the D3R 2017 community benchmarking challenge. We find that our CNN scoring function outperforms Vina on most tasks without requiring manual inspection by a knowledgeable operator, but that the pose prediction target chosen for the challenge, Cathepsin S, was particularly challenging for de novo docking. However, the CNN provided best-in-class performance on several virtual screening tasks, underscoring the relevance of deep learning to the field of drug discovery.

Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale.

Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry. Here, we review successful applications in the drug discovery domain developed over these recent years of GPU-based MD. We also retrospectively analyse limitations that have been overcome over the years and give a perspective on challenges that remain to be addressed.