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34159484	Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.	The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug dis...
38730554	BindingSiteDTI: differential-scale binding site modelling for drug-target interaction prediction.	Enhanced by contemporary computational advances, the prediction of drug-target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly...
32115529	Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning.	The goal of drug design is to discover molecular structures that have suitable pharmacological properties in vast chemical space. In recent years, the use of deep generative models (DGMs) is getting a lot of attention as an effective method of generating new molecules with desired properties. However, most of the prope...
38970161	The antibiotic resistance crisis and the development of new antibiotics.	The Global Burden of Disease report of 2019 estimated 14 million infection-related deaths, making it the second leading cause of death after ischaemic heart disease. Bacterial pathogens accounted for 7.7 million deaths and deaths attributable to bacterial antibiotic resistance amounted to 1.3 million, describing a clea...
38173000	Structure-based, deep-learning models for protein-ligand binding affinity prediction.	The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstr...
35936431	Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design.	Matched molecular pairs (MMPs) are nowadays a commonly applied concept in drug design. They are used in many computational tools for structure-activity relationship analysis, biological activity prediction, or optimization of physicochemical properties. However, until now it has not been shown in a rigorous way that MM...
38743532	TransFOL: A Logical Query Model for Complex Relational Reasoning in Drug-Drug Interaction.	Predicting drug-drug interaction (DDI) plays a crucial role in drug recommendation and discovery. However, wet lab methods are prohibitively expensive and time-consuming due to drug interactions. In recent years, deep learning methods have gained widespread use in drug reasoning. Although these methods have demonstrate...
31950132	Deep learning for drug response prediction in cancer.	Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs...
38291364	CCL-DTI: contributing the contrastive loss in drug-target interaction prediction.	The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep lea...
38516950	LGGA-MPP: Local Geometry-Guided Graph Attention for Molecular Property Prediction.	Molecular property prediction is a fundamental task of drug discovery. With the rapid development of deep learning, computational approaches for predicting molecular properties are experiencing increasing popularity. However, these existing methods often ignore the 3D information on molecules, which is critical in mole...
39038219	Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction.	Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significantly advanced the field; however, these methods exhibit limitations in predictive performance and the propensity for false negatives. Therefore, we propose EADTN, a simple ...
32193447	Improving detection of protein-ligand binding sites with 3D segmentation.	In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks lik...
32133344	<i>In silico</i> Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.	Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually...
38047362	Artificial Intelligence for the Management of Breast Cancer: An Overview.	Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various proj...
36179510	A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence.	Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing ca...
32787146	Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a Deep Convolutional Neural Network.	Rare neglected diseases may be neglected but are hardly rare, affecting hundreds of millions of people around the world. Here, we present a hit identification approach using AtomNet, the world's first deep convolutional neural network for structure-based drug discovery, to identify inhibitors targeting aspartate <i>N</...
39165116	HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.	Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Ch...
34686947	Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.	The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the availab...
34055238	Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning.	The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the <i>Coronaviridae</i> family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed ...
33866349	Deep drug-target binding affinity prediction with multiple attention blocks.	Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studie...
33041533	Applications of artificial intelligence in battling against covid-19: A literature review.	Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions ...
34957756	Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future.	Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of cardiovascular medicine, and increasingly employed to revolutionize diagnosis, treatment, risk prediction, clinical care, and drug disc...
32804486	Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach.	Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming. Therefore, prediction models based on diverse approaches have been e...
38016996	PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.	Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availab...
36899968	Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review.	Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (...
39137398	MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism.	Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they rarely consider multimodal features of drugs. Moreover, learning the i...
37752164	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...
35871213	Matrix factorization with denoising autoencoders for prediction of drug-target interactions.	Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction...
35043942	BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction.	The identification of compound-protein interactions (CPIs) is an essential step in the process of drug discovery. The experimental determination of CPIs is known for a large amount of funds and time it consumes. Computational model has therefore become a promising and efficient alternative for predicting novel interact...
37555591	De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.	Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduc...
39690486	Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.	Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the he...
34731472	Deep Neural Networks for QSAR.	Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures. These models are usua...
36970228	Machine learning-driven protein engineering: a case study in computational drug discovery.	Research and development in drug discovery will need to find significant efficiency gains if the industry is to continue generating novel drugs. There is great expectation for machine learning (ML) to provide this boost in R&D productivity, but to harness the full potential of ML, the generation of new, high-quality da...
34073705	DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.	It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D c...
35458706	Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.	In the living cells, proteins bind small molecules (or "ligands") through a "conformational selection" mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to...
38293736	Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.	Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include...
39497657	ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.	The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a si...
33707779	Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.	The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from class...
37587767	Artificial intelligence for dementia drug discovery and trials optimization.	Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing the...
33430964	Industry-scale application and evaluation of deep learning for drug target prediction.	Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Tar...
32069285	Deep Learning-driven research for drug discovery: Tackling Malaria.	Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery proc...
38466810	Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.	Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses str...
39543721	Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1.	Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. We synergistically integrate our in-hou...
36116517	Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine.	It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characterist...
36615411	Traditional Machine and Deep Learning for Predicting Toxicity Endpoints.	Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for applying deep learning ...
35443040	D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.	Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug ...
36736583	New avenues in artificial-intelligence-assisted drug discovery.	Over the past decade, the amount of biomedical data available has grown at unprecedented rates. Increased automation technology and larger data volumes have encouraged the use of machine learning (ML) or artificial intelligence (AI) techniques for mining such data and extracting useful patterns. Because the identificat...
33482713	SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks.	Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to t...
33347838	Artificial intelligence in the early stages of drug discovery.	Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recentl...
32986405	Deep-Learning-Assisted Assessment of DNA Damage Based on Foci Images and Its Application in High-Content Screening of Lead Compounds.	DNA damage is one of major culprits in many complex diseases; thus, there is great interest in the discovery of novel lead compounds regulating DNA damage. However, there remain plenty of challenges to evaluate DNA damage through counting the amount of intranuclear foci. Herein, a deep-learning-based open-source pipeli...
38709146	MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions.	Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided i...
33166392	LitCovid: an open database of COVID-19 literature.	Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10,000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general ...
38332817	The pursuit of accurate predictive models of the bioactivity of small molecules.	Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true ...
36204314	Editorial: Machine learning for peptide structure, function, and design.
37111744	Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives.	Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, of...
34731482	Machine Learning for In Silico ADMET Prediction.	ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety are considered some of the major causes of clinical attrition in the...
32452701	An up-to-date overview of computational polypharmacology in modern drug discovery.	In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and a...
39517982	Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution.	This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet M...
36967455	Deep Learning-Based Modeling of Drug-Target Interaction Prediction Incorporating Binding Site Information of Proteins.	Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study ai...
39360948	Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery.	Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out...
34083538	Crowdsourced mapping of unexplored target space of kinase inhibitors.	Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. He...
33482718	Mining influential genes based on deep learning.	Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling ...
35294204	Machine Learning Analysis of Cocaine Addiction Informed by DAT, SERT, and NET-Based Interactome Networks.	Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large number of deaths around the world. Despite decades of effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involv...
35711777	Machine Learning Advances in Microbiology: A Review of Methods and Applications.	Microorganisms play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimi...
39173285	MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.	Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in accelerating antimalarial drug discovery, the performance of deep lea...
36169035	An interpretable machine learning model for selectivity of small-molecules against homologous protein family.	<b>Aim:</b> In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. <b>Methods:</b> We have developed a multitask deep...
37191131	Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery.	We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demonstrated by inhibition assays using a PPI target, Kelch-like ECH-assoc...
35994925	A deep learning method for predicting molecular properties and compound-protein interactions.	Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges....
39177478	SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.	Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the op...
38874046	Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.	Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis develop...
34664074	MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.	Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA seque...
33058459	Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding.	Deep learning-based methods have been extensively developed to improve scoring performance in structure-based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task network. Recently, grid featurization has been introduced to convert protei...
38215530	AI for targeted polypharmacology: The next frontier in drug discovery.	In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such dru...
37215152	A review from biological mapping to computation-based subcellular localization.	Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzhei...
38161364	Geometric Deep Learning for Structure-Based Ligand Design.	A pervasive challenge in drug design is determining how to expand a ligand-a small molecule that binds to a target biomolecule-in order to improve various properties of the ligand. Adding single chemical groups, known as fragments, is important for lead optimization tasks, and adding multiple fragments is critical for ...
40099205	Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.	Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the ...
38925955	Structure-based computational design of antibody mimetics: challenges and perspectives.	The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of thei...
39604569	SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.	Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employ...
33355332	Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.	Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists ...
34670583	Prediction of synergistic drug combinations using PCA-initialized deep learning.	Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space make...
35578204	Optimization of the antimicrobial peptide Bac7 by deep mutational scanning.	Intracellularly active antimicrobial peptides are promising candidates for the development of antibiotics for human applications. However, drug development using peptides is challenging as, owing to their large size, an enormous sequence space is spanned. We built a high-throughput platform that incorporates rapid inve...
36160045	Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models.	Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offering a more efficient drug-discovery route. However, unsolved technical bottlenecks of reproducibility and scalability have prevented the use of current organoids for high...
39026630	Leveraging bounded datapoints to classify molecular potency improvements.	Molecular machine learning algorithms are becoming increasingly powerful at predicting the potency of potential drug candidates to guide molecular discovery, lead series prioritization, and structural optimization. However, a substantial amount of inhibition data is bounded and inaccessible to traditional regression al...
32697578	A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery.	Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal. The use of deep-learning technologies could augment the ...
38662768	An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives.	There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pi...
38972499	RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints.	Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The a...
38577892	Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.	Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore bindin...
38925473	Advancing drug discovery with deep attention neural networks.	In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), g...
39508428	Challenges and Prospects of DNA-Encoded Library Data Interpretation.	DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets...
38669740	Deep learning for low-data drug discovery: Hurdles and opportunities.	Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches-which are notoriously 'data-hungry...
37506452	Topological deep learning based deep mutational scanning.	High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, ...
39169261	Deep learning large-scale drug discovery and repurposing.	Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and m...
39020064	In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy.	Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medication for epilepsy treatment remains an urgent and significant challenge. Drug repurposing emerges as a cost-effective and efficient strategy to combat this disorder. This study leverages the transformer-based deep learnin...
37550462	Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.	Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance...
39148721	Malivhu: A Comprehensive Bioinformatics Resource for Filtering SARS and MERS Virus Proteins by Their Classification, Family and Species, and Prediction of Their Interactions Against Human Proteins.	COVID 19 pandemic is still ongoing, having taken more than 6 million human lives with it, and it seems that the world will have to learn how to live with the virus around. In consequence, there is a need to develop different treatments against it, not only with vaccines, but also new medicines. To do this, human-virus ...
38166249	Advancing Antibody Engineering through Synthetic Evolution and Machine Learning.	Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the...
33808317	iEnhancer-GAN: A Deep Learning Framework in Combination with Word Embedding and Sequence Generative Adversarial Net to Identify Enhancers and Their Strength.	As critical components of DNA, enhancers can efficiently and specifically manipulate the spatial and temporal regulation of gene transcription. Malfunction or dysregulation of enhancers is implicated in a slew of human pathology. Therefore, identifying enhancers and their strength may provide insights into the molecula...
36290108	Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.	Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spe...
36516300	A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.	Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their appl...
34526388	Deep learning identifies synergistic drug combinations for treating COVID-19.	Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxi...