Computational Discovery Research Articles

Computational Phase Discovery in Block Polymers.

Self-consistent field theory (SCFT), the mean-field theory of polymer thermodynamics, is a powerful tool for understanding ordered state selection in block copolymer melts and blends. However, the nonlinear governing equations pose a significant challenge when SCFT is used for phase discovery because converging an SCFT solution typically requires an initial guess close to the self-consistent solution. This Viewpoint provides a concise overview of recent efforts where machine learning methods (particle swarm optimization, Bayesian optimization, and generative adversarial networks) have been used to make the first strides toward converting SCFT from a primarily explanatory tool into one that can be readily deployed for phase discovery.

Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization.

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer to improve the computational efficiency of geometry optimization. Compared to previous active learning approaches, our algorithm adds two key features: a modified delta method incorporating force information to enhance efficiency in uncertainty estimation, and a quasi-Newton approach based upon a Hessian matrix calculated from the neural network; the later improving stability of optimization near critical points. We benchmarked our optimizer against commonly used optimization algorithms using systems including bulk metal, metal surface, metal hydride, and an oxide cluster. The results demonstrate that our optimizer effectively reduces the number of DFT force calls by 2-3 times in all test systems.

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 for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

Molecular insights from structural dynamics of HER2-inhibitor complexes pave the way for new breast cancer drugs

The human epidermal growth factor receptor 2 (HER2) is closely associated with the development and progression of breast cancer, making it a critical target for therapeutic interventions. In this study, we employed a comprehensive computational drug discovery strategy to identify potential inhibitors of HER2. Our approach combined virtual screening, re-docking procedures, molecular dynamics (MD) simulations, and free energy landscape analysis using principal component analysis (PCA). From the extensive PubChem library, we initially screened 733 compounds for their binding potential to HER2, using docking scores as a primary filter. These scores ranged notably from −11.172 to −7.028 kcal/mol, indicating substantial binding capacities. Following this screening, we selected four promising compounds (PubChem CID 166029206, 166544027, 21031510, and 11712721) along with a control compound (70I) for in-depth analysis. Utilizing the Amber software suite for MD simulations, we conducted 200-nanosecond simulations to assess the interactions and binding efficiencies of these selected compounds with HER2. We analysed the molecular interactions through various parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond formation patterns, free binding energy calculations. The PCA-based free energy landscape analysis revealed that these compounds consistently occupied a distinct low-energy basin, indicating their high stability and strong binding affinity for the HER2. This detailed analysis provided insights into the stability and conformational dynamics of these potential inhibitors when bound to the HER2. Our findings pave the way for further experimental validation and development of these compounds as therapeutic agents in breast cancer treatment.

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2

An Extensive Computational Investigation of Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors from Diverse‐Lib Compounds Library

AbstractAntibiotics have played a crucial role in significantly reducing the incidence of tuberculosis (TB) infection worldwide. Even before the mid‐20th century, the mortality rate of TB onset within five years was around 50 %. So, the introduction of antibiotics has changed the scenario of TB from a serious threat to a manageable one. However, the emergence of resistance to anti‐TB drugs poses a significant challenge. So, to overcome this situation the therapeutic approaches and drug targets need to be reformed. This study focused on finding potential inhibitors by targeting Pantothenate Synthetase, a crucial enzyme for Mycobacterium tuberculosis (Mtb) survival, through computational drug discovery methods. Molecular docking and virtual screening were employed to identify potential inhibitors from Diverse‐lib. Four compounds, namely CID2813602, 24357538, CID753354, and CID4798023, exhibited strong binding energies and stable interaction with the target protein. Further assessment of these compounds through MD simulation and Post MD simulation showed significant dynamic stability. The minimum energy transition calculated using the free energy landscape analysis of these compounds when docked with Pantothenate Synthetase confirmed the stability of each complex due to its minimum energy production. The free binding energy calculation of each complex also showed the intramolecular interaction contributes to the strong binding affinity of the compounds within the enzyme's active site clarifying their mechanisms of action. This research showcases the effectiveness of computational methods in promptly identifying potential anti‐TB drugs, paving the way for future experimental validation and optimization. It holds promise for the development of new treatments targeting drug‐resistant TB strains.

Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility.

Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks

In the field of biomedical engineering, predicting Drug-Target Affinities (DTA) is crucial. However, current affinity prediction models are predominantly manually designed, which is a complex, time-consuming process that may not effectively accommodate the diversity and complexity of datasets. To address this challenge, we propose an adaptive learning framework for predicting drug-target affinity, called Adaptive-DTA, which integrates reinforcement learning with graph neural networks to automate the design of affinity prediction models. Adaptive-DTA defines the architecture search space using directed acyclic graphs and employs an reinforcement learning algorithm to guide the architecture search, optimizing parameters based on the entropy of sampled architectures and model performance metrics. Additionally, we enhance efficiency with a two-stage training and validation strategy, incorporating low-fidelity and high-fidelity evaluations. Our framework not only alleviates the challenges associated with manual model design but also significantly improves model performance and generalization. To evaluate the performance of our method, we conducted extensive experiments on DTA benchmark datasets and compared the results with nine state-of-the-art methods. The experimental outcomes demonstrate that our proposed framework outperforms these methods, exhibiting outstanding performance in predicting drug-target affinities. Our innovative approach streamlines the design of affinity prediction model, reduces reliance on manual crafting, and enhances model generalization. Its ability to automatically optimize network architectures represents a major step forward in the automation of computational drug discovery processes.

Computational applications for the discovery of novel antiperovskites and chalcogenide perovskites: a review.

Novel perovskites pertain to newly discovered or less studied variants of the conventional perovskite structure, characterized by distinctive properties and potential for diverse applications such as ferroelectric, optoelectronic, and thermoelectric uses. In recent years, advancements in computational methods have markedly expedited the discovery and design of innovative perovskite materials, leading to numerous pertinent reports. However, there are few reviews that thoroughly elaborate the role of computational methods in studying novel perovskites, particularly for state-of-the-art perovskite categories. This review delves into the computational discovery of novel perovskite materials, with a particular focus on antiperovskites and chalcogenide perovskites. We begin with a discussion on the computational methods applied to evaluate the stability and electronic structure of materials. Next, we highlight how these methods expedite the discovery process, demonstrating how rational simulations contribute to researching novel perovskites with improved performance. Finally, we thoroughly discuss the remaining challenges and future outlooks in this research domain to encourage further investigation. We believe that this review will be highly beneficial both for newcomers to the field and for experienced researchers in computational science who are shifting their focus to novel perovskites.

Development of novel dual-target drugs against visceral leishmaniasis and combinational study with miltefosine

The dual-target inhibitors (ZINC000008876351 and ZINC000253403245) were identified by utilizing an advanced computational drug discovery method by targeting two critical enzymes such as FeSODA (Iron superoxide dismutase) and TryR (Trypanothione reductase) within the antioxidant defense system of Leishmania donovani (Ld). In vitro enzyme inhibition kinetics reveals that both the compound's ability to inhibit the function of enzyme LdFeSODA and LdTryR with inhibition constant (Ki) value in the low μM range. Flow cytometry analysis, specifically at IC50 and 2X IC50 doses of both the compounds, the intracellular ROS was significantly increased as compared to the untreated control. The compounds ZINC000253403245 and ZINC000008876351 exhibited strong anti-leishmanial activity in a dose-dependent manner against both the promastigote and amastigote stages of the parasite. The data indicate that these molecules hold promise as potential anti-leishmanial agents for developing new treatments against visceral leishmaniasis, specifically targeting the LdFeSODA and LdTryR enzymes. Additionally, the in vitro MTT assay shows that combining these compounds with miltefosine produces a synergistic effect compared to miltefosine alone. This suggests that the compounds can boost miltefosine's effectiveness by synergistically inhibiting the growth of L. donovani promastigotes. Given the emergence of miltefosine resistance in some Leishmania strains, these findings are particularly significant.

Implementation of an Automated System Using Machine Learning Models to Accelerate the Process of In Silico Identification of Small Molecules As Drug Candidates.

Drugs are commonly utilized to diagnose, cure, or prevent the occurrence of diseases, as well as to restore, alter, or change organic functions. Drug discovery is a time-consuming, costly, difficult, and inefficient process that yields very few medicinal breakthroughs. Drug research and design involves the capturing of structural information for biological targets and small molecules as well as various in silico methods, such as molecular docking and molecular dynamic simulation. This article proposes the idea of expediting computational drug development through a collaboration of scientists and universities, similar to the Human Genome Project using machine learning (ML) strategies. We envision an automated system where readily available or novel small molecules (chemical or plant-derived), as well as their biological targets, are uploaded to an online database, which is constantly updated. For this system to function, machine learning strategies have to be implemented, and high-quality datasets and high quality assurance of the ML models will be required. ML can be applied to all computational drug discovery fields, including hit discovery, target validation, lead optimization, drug repurposing, and data mining of small compounds and biomolecule structures. Researchers from various disciplines, such as bioengineers, bioinformaticians, geneticists, chemists, computer and software engineers, and pharmacists, are expected to collaborate to establish a solid workflow and certain parameters as well as constraints for a successful outcome. This automated system may help speed up the drug discovery process while also lowering the number of unsuccessful drug candidates. Additionally, this system will decrease the workload, especially in computational studies, and expedite the process of drug design. As a result, a drug may be manufactured in a relatively short time.

Elucidating the role of PPARG inhibition in enhancing MERS virus immune response: A network pharmacology and computational drug discovery

BackgroundMiddle East Respiratory Syndrome (MERS) has become a severe zoonotic disease, posing significant public health concerns due to the lack of specific medications. This urgently demands the development of novel therapeutic molecules. Understanding MERS's genetic underpinnings and potential therapeutic targets is crucial for developing effective treatments. MethodsTwo gene expression datasets (GSE81909 and GSE100504) were analyzed to identify differentially expressed genes (DEGs) using GEO2R. Furthermore, gene ontology (GO), pathway enrichment analysis, and protein-protein interaction (PPI) network were performed to understand the gene’s functions. A possible drug target was identified, and an FDA-approved drug library was screened against the selected target using molecular docking and validated the findings through molecular dynamics simulation, principal component analysis, free energy landscape, and MM/GBSA calculations. ResultsThe study on GSE81909 and GSE100504 datasets with icMERS and MOCK samples at 24 and 48 h revealed an upregulation in 73 and 267 DEGs, respectively. In the network pharmacology, STAT1, MX1, DDX58, EIF2AK2, ISG15, IFIT1, IFIH1, OAS1, IRF9, and OASL were identified as the top 10 hub genes. STAT1 was identified as the most connected hub gene among these top 10 hub genes, which plays a crucial role in the immune response to the MERS virus. Further study on STAT1 showed that PPARG helps reduce STAT1, which could modulate the immune response. Therefore, by inhibiting PPARG, the immunological response can be successfully enhanced. The known inhibitor of PPARG, 570 (Farglitazar), was used as a control. Further, screening using Tanimoto and K-mean clustering was performed, from which three compounds were identified: 2267, 3478, and 40326. Compound 3478 showed characteristics similar to the control, indicating robust binding to PPARG. 3478 showed the highest negative binding free energy with −41.20 kcal/mol, indicating strong binding with PPARG. ConclusionsThese findings suggest that 3478 promises to be a potential inhibitor of PPARG, and further experimental investigations can explore its potential as a MERS inhibitor.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Viromes vs. mixed community metagenomes: choice of method dictates interpretation of viral community ecology

BackgroundViruses, the majority of which are uncultivated, are among the most abundant biological entities on Earth. From altering microbial physiology to driving community dynamics, viruses are fundamental members of microbiomes. While the number of studies leveraging viral metagenomics (viromics) for studying uncultivated viruses is growing, standards for viromics research are lacking. Viromics can utilize computational discovery of viruses from total metagenomes of all community members (hereafter metagenomes) or use physical separation of virus-specific fractions (hereafter viromes). However, differences in the recovery and interpretation of viruses from metagenomes and viromes obtained from the same samples remain understudied.ResultsHere, we compare viral communities from paired viromes and metagenomes obtained from 60 diverse samples across human gut, soil, freshwater, and marine ecosystems. Overall, viral communities obtained from viromes had greater species richness and total viral genome abundances than those obtained from metagenomes, although there were some exceptions. Despite this, metagenomes still contained many viral genomes not detected in viromes. We also found notable differences in the predicted lytic state of viruses detected in viromes vs metagenomes at the time of sequencing. Other forms of variation observed include genome presence/absence, genome quality, and encoded protein content between viromes and metagenomes, but the magnitude of these differences varied by environment.ConclusionsOverall, our results show that the choice of method can lead to differing interpretations of viral community ecology. We suggest that the choice of whether to target a metagenome or virome to study viral communities should be dependent on the environmental context and ecological questions being asked. However, our overall recommendation to researchers investigating viral ecology and evolution is to pair both approaches to maximize their respective benefits.ADm-mTTAXM3aVZwCyoroCBVideo

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules.

Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO2 and CH4 adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs.

A deep learning framework combining molecular image and protein structural representations identifies candidate drugs for pain

Artificial intelligence (AI) and deep learning technologies hold promise for identifying effective drugs for human diseases, including pain. Here, we present an interpretable deep-learning-based ligand image- and receptor's three-dimensional (3D)-structure-aware framework to predict compound-protein interactions (LISA-CPI). LISA-CPI integrates an unsupervised deep-learning-based molecular image representation (ImageMol) of ligands and an advanced AlphaFold2-based algorithm (Evoformer). We demonstrated that LISA-CPI achieved ∼20% improvement in the average mean absolute error (MAE) compared to state-of-the-art models on experimental CPIs connecting 104,969 ligands and 33 G-protein-coupled receptors (GPCRs). Using LISA-CPI, we prioritized potential repurposable drugs (e.g., methylergometrine) and identified candidate gut-microbiota-derived metabolites (e.g., citicoline) for potential treatment of pain via specifically targeting human GPCRs. In summary, we presented that the integration of molecular image and protein 3D structural representations using a deep learning framework offers a powerful computational drug discovery tool for treating pain and other complex diseases if broadly applied.

Mechanistic and dynamic insight into novel IL7 receptor activators as immunotherapy for the treatment of tuberculosis

The bacterium that causes tuberculosis (TB), Mycobacterium tuberculosis, is still a significant global public health problem due to its contagious nature and the emergence of drug-resistant variants. This study leverages network pharmacology and computational drug discovery to identify genes linked to TB using the GSE54992 and GSE62525 datasets. In active tuberculosis patients, it identifies 162 DEGs that are not present in healthy people. The research includes an investigation of gene ontology (GO) and protein–protein interaction (PPI) networks to provide a comprehensive understanding of these DEGs. Crucially, it identifies ten hub genes: IL7R, CCR7, CXCR5, CCR6, CCR9, S1PR1, BACH2, CXCL5, SPIB, and EBF. The research suggests IL7R as a potential therapeutic target because of its essential role in FoxO signalling pathways and cytokine-cytokine receptor interaction. IL7 and its receptor (IL7R) are vital for T cell functionality and essential in combating TB. The study explores novel small-molecule activators for IL7R (IL7Rα), aiming to boost the immune response against TB. The following four compounds were found using molecular dynamics simulation, virtual screening, computational pharmacophore modelling, and MM/GBSA analytical techniques: ZINC02131028, ZINC02135418, ZINC46007405, and ZINC59273354. These activators may enhance the IL7 signalling pathway, potentially strengthening the immune fight against M. tuberculosis. Among these ZINC02131028 showed the best results to activate IL7 by binding to its active site, stabilizing its active conformation and enhancing its interaction with the IL7 receptor (IL7R). The findings encourage experimental validation of these IL7 activators, marking a significant step towards innovative TB treatment strategies.

DTI-LM: language model powered drug-target interaction prediction.

The identification and understanding of drug-target interactions (DTIs) play a pivotal role in the drug discovery and development process. Sequence representations of drugs and proteins in computational model offer advantages such as their widespread availability, easier input quality control, and reduced computational resource requirements. These make them an efficient and accessible tools for various computational biology and drug discovery applications. Many sequence-based DTI prediction methods have been developed over the years. Despite the advancement in methodology, cold start DTI prediction involving unknown drug or protein remains a challenging task, particularly for sequence-based models. Introducing DTI-LM, a novel framework leveraging advanced pretrained language models, we harness their exceptional context-capturing abilities along with neighborhood information to predict DTIs. DTI-LM is specifically designed to rely solely on sequence representations for drugs and proteins, aiming to bridge the gap between warm start and cold start predictions. Large-scale experiments on four datasets show that DTI-LM can achieve state-of-the-art performance on DTI predictions. Notably, it excels in overcoming the common challenges faced by sequence-based models in cold start predictions for proteins, yielding impressive results. The incorporation of neighborhood information through a graph attention network further enhances prediction accuracy. Nevertheless, a disparity persists between cold start predictions for proteins and drugs. A detailed examination of DTI-LM reveals that language models exhibit contrasting capabilities in capturing similarities between drugs and proteins. Source code is available at: https://github.com/compbiolabucf/DTI-LM.

Computational discovery of fast interstitial oxygen conductors.

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La4Mn5Si4O22+δ, a representative member of the perrierite/chevkinite family. We found that La4Mn5Si4O22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO2, and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

A time‐efficient computational binding affinity estimation protocol with utilization of limited experimental data: A case study for adenosine receptor

AbstractEstimating binding affinity is a crucial step in the drug discovery process. In computer‐aided drug design, this challenge can be divided into two main tasks: finding the correct binding pose and estimating the binding free energy. In this study, we propose a new binding affinity estimation protocol that utilizes molecular docking with limited experimental data and estimates binding affinity using molecular dynamics simulation. A custom scoring function was employed during docking to identify an improved initial binding pose, and the linear interaction energy method with an optimized coefficient was used for binding affinity estimation. The protocol was validated with an external data set and applied to modafinil and its derivatives to rank their binding affinities to adenosine A2A receptors (ADORA2A) as a case study. This approach could be both time‐efficient and valuable for computational drug discovery, particularly when experimental data is limited.