Drug Discovery Methods Research Articles

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules’ simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.

Bioactive compounds from Ocimum tenuiflorum and Poria cocos: A novel natural Compound for insomnia treatment based on A computational approach

Insomnia, a widespread public health issue, is associated with substantial distress and daytime functionality impairments and can predispose to depression and cardiovascular disease. Cognitive Behavioral Anti-insomnia therapies including benzodiazepines often face limitations due to patient adherence or potential adverse effects. This study focused on identifying novel bioactive compounds from medicinal plants, aiming to discover and develop new therapeutic agents with low risk-to-benefit ratios using computational drug discovery methods. Through a systematic framework involving compound library preparation, evaluation of drug-likeness and pharmacokinetics, toxicity prediction, molecular docking, and molecular dynamic simulations, two natural compounds such as 2-(4-hydroxy-3-methoxyphenyl)-8-methoxy-6-prop-2-enyl-3,4-dihydro-2H-chromen-3-ol from Ocimum tenuiflorum and 7-(2-hydroxypropan-2-yl)-1,4a-dimethyl-9-oxo-3,4,10,10a-tetrahydro-2H-phenanthrene-1-carboxylic acid from Poria cocos exhibited high binding affinity with orexin receptor type 1 (OX1R) and type 2 (OX2R), surpassing commercial drugs used in insomnia treatment. Additionally, they showed interactions with critical amino acid residues within the receptors that play crucial roles in competitive inhibitor activity, like commercial drugs such as Suvorexant, Lemborexant, and Daridorexant. Further, molecular dynamics simulations of the protein-ligand complexes under conditions that mimic the in vivo environment revealed both compounds' sustained and robust interactions with the OX1R and OX2R, reinforcing their potential as effective therapeutic candidates. Furthermore, upon evaluating both compounds' drug-likeness, pharmacokinetics, and toxicity profiles, it was discerned that they displayed considerable drug-like properties and favorable pharmacokinetics, along with diminished toxicity. The research provides a solid foundation for further exploring and validating these compounds as potential anti-insomnia therapeutics.

Exploring machine learning for untargeted metabolomics using molecular fingerprints

BackgroundMetabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. MethodsThis study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups. ResultsThe approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study. ConclusionIn conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

Analysing the Anticancer Properties of Pterostilbene Through Absorption, Distribution, Metabolism, and Excretion (ADME) and Molecular Docking Studies.

Aim The aim of this study is to examine the possible therapeutic effect of pterostilbene (PTS), a chemical present in grapes and blueberries, in the treatment of liver cancer by analysing its interactions with important proteins linked to the wingless/integrated (Wnt) signaling system. Objective Using computational techniques like molecular docking and absorption, distribution, metabolism, and excretion (ADME) studies, this research focuses on examining the pharmacokinetics and molecular interactions of PTS with proteins such as vimentin (Vim), glycogen synthase kinase 3 beta (GSK3-β), epithelial cadherin (E-cadherin), interleukin-6 (IL-6), interleukin-1 beta (IL-1β), c-Jun N-terminal kinase (JNK), and Wnt, all of which are connected to the Wnt signaling pathway in liver cancer. Methods The study includes the synthesis of proteins and ligands, ADME investigations for PTS, and AutoDock Vina molecular docking simulations to evaluate binding affinities and interactions. PTS is obtained from PubChem, while protein structures are obtained from the Protein Data Bank. Results Strong binding affinities between PTS and essential proteins in the Wnt signaling cascade are shown by molecular docking, which also highlights noteworthy hydrogen bonds, hydrophobic interactions, and electrostatic contacts. According to an ADME study, PTS has advantageous pharmacokinetic properties, such as moderate solubility, membrane permeability, and a minimal chance of drug interactions. Conclusion The extensive study highlights PTS's potential as a viable treatment option for liver cancer. The study promotes its investigation in cutting-edge liver cancer therapy approaches and urges more investigation into the molecular mechanisms, underpinning its anticancer properties. This paper sheds important light on the role of natural chemicals in cancer therapy and emphasizes the need for computational methods in drug discovery.

Cryo-electron microscopy-based drug design.

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Utilizing Artificial Intelligence in the Development of Medicinal Drugs: Practical Applications

The COVID-19 pandemic has underscored the critical need for innovative drug discovery methods. The journey from conceptualizing a drug to its clinical application is fraught with potential pitfalls, including extensive complexity, significant expenses, and a high risk of failure. Recent years have witnessed remarkable advancements in technologies such as cloud computing, GPUs, and TPUs. These developments, coupled with the surge in medical data availability and the emergence of deep learning, present an unprecedented opportunity to enhance drug discovery processes. By leveraging artificial intelligence (AI) to analyze vast amounts of data-from extensive molecular screening outcomes to individual health records and public health data-the efficiency of the drug discovery pipeline could be significantly improved, minimizing the likelihood of failure. This paper explores the application of AI in various phases of drug development, including the use of computational strategies for de novo drug design and the prediction of drug properties. We address challenges associated with molecular representation, data acquisition, complexity, and the inconsistencies in labeling across open-source databases and AI-powered tools that support drug discovery efforts. Furthermore, we examine the role of advanced AI techniques, such as graph neural networks, reinforcement learning, generative models, and structure-based methods like molecular docking and dynamics simulations, in enhancing drug discovery and evaluating drug efficacy.

HIGH THROUGHPUT SCREENING IN DRUG DISCOVERY: PROBLEMS AND SOLUTIONS

World-wide introduction of high throughput screening (HTS) methods in drug discovery research did not result in the increased number of novel medications on the market. We discuss novel trends in drug discovery that came from the understanding that majority of diseases are multifactorial and that one enzyme has many protein substrates. Hence, new approaches are focused on development of drugs, which (1) trigger survival pathways to return the organism to homeostatic balance, and (2) inhibit enzymes modifying histones or transcription factors not at the active site, but by displacement of protein substrates from the enzyme complexes. A good example for both approaches comes from the development of activators of antioxidant defense. We analyze and illustrate problems of commonly used in vitro HTS assays, and briefl y discuss advantages and limitations of small animal models. The novel approaches are complementary to the standard HTS and do not substitute for testing in mammals. Development of transgenic reporter mice to monitor drug effects by means of in vivo imaging is extremely promising to select proper dosage and administration regimes for full-range PK studies.

VIBRANT: a phenotyping method for drug discovery using vibrational spectroscopy.

VIBRANT: a phenotyping method for drug discovery using vibrational spectroscopy.

GSL-DTI: Graph structure learning network for Drug-Target interaction prediction

Motivation: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. ResultsWe propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.

In Silico Exploration of APOE4 Inhibitors: Molecular Docking and ADMET Profiling for Alzheimer's Therapy

Background: Alzheimer's disease (AD), a prevalent neurodegenerative disorder, poses significant challenges to healthcare systems worldwide. The aggregation of Amyloid-β peptide and the presence of apolipoprotein E4 (apoE4) are recognized as central factors in AD pathogenesis. Despite the availability of FDA-approved drugs, there remains a dire need for more effective and targeted treatments to combat this disease. The advent of in silico methodologies offers a promising avenue for accelerating the drug discovery process, providing a cost-effective and efficient means to screen for potential therapeutic agents. Objective: This study aimed to identify and evaluate potential inhibitors of apoE4 using in silico drug discovery methods. By targeting the interaction domain of apoE4, the study sought to discover compounds capable of reducing beta-amyloid aggregation, thus offering a novel approach to AD therapy. Methods: Computational techniques, including the Lipinski Rule of Five and ADMET filtering, were employed to screen a dataset of 80 chemical inhibitors retrieved from the NCBI database. Molecular docking was performed to assess the binding affinities and orientations of the compounds to apoE4, utilizing Phyre2 for 3D structure prediction and AutoDock Vina for docking simulations. The selection criteria for lead compounds were based on their binding energy, hydrogen bonding interactions, and compliance with drug-like properties. Results: Out of the initial 80 compounds screened, 15 were excluded based on the Lipinski Rule of Five, and an additional 43 were deemed unsuitable following ADMET analysis. Molecular docking identified two compounds, taxifolin and luteolin, exhibiting superior binding affinities to apoE4, with taxifolin showing the lowest binding energy of -7.4 kcal/mol and the highest number of hydrogen bonds in one of its conformations. Conclusion: The study successfully leveraged in silico methods to identify taxifolin and luteolin as potent inhibitors of apoE4, highlighting the potential of computational drug discovery in developing novel AD therapies. These findings pave the way for future experimental validation and clinical trials to assess the therapeutic efficacy and safety of these compounds in treating Alzheimer's disease.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Construction of microgravity biological knowledge graph and its applications in anti-osteoporosis drug prediction

Microgravity in the space environment can potentially have various negative effects on the human body, one of which is bone loss. Given the increasing frequency of human space activities, there is an urgent need to identify effective anti-osteoporosis drugs for the microgravity environment. Traditional microgravity experiments conducted in space suffer from limitations such as time-consuming procedures, high costs, and small sample sizes. In recent years, the in-silico drug discovery method has emerged as a promising strategy due to the advancements in bioinformatics and computer technology. In this study, we first collected a total of 184,915 literature articles related to microgravity and bone loss. We employed a combination of dependency path extraction and clustering techniques to extract data from the text. Afterwards, we conducted data cleaning and standardization to integrate data from several sources, including The Global Network of Biomedical Relationships (GNBR), Curated Drug–Drug Interactions Database (DDInter), Search Tool for Interacting Chemicals (STITCH), DrugBank, and Traditional Chinese Medicines Integrated Database (TCMID). Through this integration process, we constructed the Microgravity Biology Knowledge Graph (MBKG) consisting of 134,796 biological entities and 3,395,273 triplets. Subsequently, the TransE model was utilized to perform knowledge graph embedding. By calculating the distances between entities in the model space, the model successfully predicted potential drugs for treating osteoporosis and microgravity-induced bone loss. The results indicate that out of the top 10 ranked western medicines, 7 have been approved for the treatment of osteoporosis. Additionally, among the top 10 ranked traditional Chinese medicines, 5 have scientific literature supporting their effectiveness in treating bone loss. Among the top 20 predicted medicines for microgravity-induced bone loss, 15 have been studied in microgravity or simulated microgravity environments, while the remaining 5 are also applicable for treating osteoporosis. This research highlights the potential application of MBKG in the field of space drug discovery.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies.

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Targeting ion channels with ultra-large library screening for hit discovery.

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Combining AI and traditional screening for discovery of a potent ROCK2 inhibitor against lymphoma

The AGC family serine/threonine kinase, ROCKs, is essential in various biological processes, such as cell contraction, migration, and morphological changes. The ROCKs family, consisting of two main members, ROCK1 and ROCK2, shares similarities but possesses distinct functions. In recent years, the therapeutic potential of ROCK2 in the anti-tumor field, especially in the field of leukemia, has received widespread attention from researchers. In this study, a combination of traditional methods and advanced AI-driven techniques, including the AI-based Deepdock algorithm following conventional screening, was employed for molecule discovery to enhance the efficacy of virtual screening. This combined approach led to the identification of several potential ROCK2 inhibitor molecules. One of these, compound 5, demonstrated a notable inhibitory effect, with an IC50 of 35.66 nM in the kinase ADP-GLO assay. Subsequent tests on lymphoma cell lines validated its cytotoxic efficacy, especially in the Z138 human lymphoma cell line, where a significant killing effect at 19 nM was observed. This research underscores the potential of combining AI with traditional screening methods in drug discovery and highlights a promising ROCK2 inhibitor for lymphoma treatment.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods.

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50,DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50,MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50,DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50,DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50,MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Interaction of Acinetobacter sp. RIT 592 induces the production of broad-spectrum antibiotics in Exiguobacterium sp. RIT 594.

Antimicrobial resistance (AMR) is one of the most alarming global public health challenges of the 21st century. Over 3 million antimicrobial-resistant infections occur in the United States annually, with nearly 50,000 cases being fatal. Innovations in drug discovery methods and platforms are crucial to identify novel antibiotics to combat AMR. We present the isolation and characterization of potentially novel antibiotic lead compounds produced by the cross-feeding of two rhizosphere bacteria, Acinetobacter sp. RIT 592 and Exiguobacterium sp. RIT 594. We used solid-phase extraction (SPE) followed by liquid chromatography (LC) to enrich antibiotic extracts and subsequently mass spectrometry (MS) analysis of collected fractions for compound structure identification and characterization. The MS data were processed through the Global Natural Product Social Molecular Networking (GNPS) database. The supernatant from RIT 592 induced RIT 594 to produce a cocktail of antimicrobial compounds active against Gram-positive and negative bacteria. The GNPS analysis indicated compounds with known antimicrobial activity in the bioactive samples, including oligopeptides and their derivatives. This work emphasizes the utility of microbial community-based platforms to discover novel clinically relevant secondary metabolites. Future work includes further structural characterization and antibiotic activity evaluation of the individual compounds against pathogenic multidrug-resistant (MDR) bacteria.

DrugPose: benchmarking 3D generative methods for early stage drug discovery

Molecule generation in 3D space has gained attention in the past few years.

AI-based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.

Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.