☆ 4.8 Review

Computational approaches streamlining drug discovery

NATURE (2023)

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Review Chemistry, Multidisciplinary

AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors

Xin-heng He et al.

Summary: G protein-coupled receptors (GPCRs) are important drug targets that play crucial roles in various physiological processes. Although extensive efforts have been made in the field of structural biology, a significant number of GPCR structures remain unsolved due to their structural instability. Recently, AlphaFold2 has been developed as a tool to predict the structure models of GPCRs and other functionally important proteins. However, our evaluation reveals several differences between the predicted models and experimental structures, such as the assembly of domains, shape of ligand-binding pockets, and conformation of binding interfaces. These differences hinder the use of predicted structure models in functional studies and structure-based drug design, where reliable high-resolution structural information is required.

ACTA PHARMACOLOGICA SINICA (2023)

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Article Biochemistry & Molecular Biology

Modeling the expansion of virtual screening libraries

Jiankun Lyu et al.

Summary: Recently, 'tangible' virtual libraries have made billions of molecules readily available, but their prioritization for synthesis and testing requires considering their diversity, similarity to bio-like molecules, and changes in receptor fit and artifacts with library size. Comparisons were made between a 3 million 'in-stock' molecules library and billion-plus tangible libraries. The tangible library showed a 19,000-fold decrease in preference for bio-like molecules compared to the 'in-stock' library. High-ranking molecules from docking campaigns in ultra-large libraries were also dissimilar to bio-like molecules. However, better-fitting molecules were found as the library size increased, improving the score logarithmically.

NATURE CHEMICAL BIOLOGY (2023)

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Article Biochemical Research Methods

AlphaFill: enriching AlphaFold models with ligands and cofactors

Maarten L. Hekkelman et al.

Summary: Artificial intelligence-based protein structure prediction has greatly impacted biomolecular sciences. However, the predicted protein models in the AlphaFold database lack coordinates for small molecules and ions necessary for structure and function. The AlphaFill algorithm addresses this issue by transplanting missing small molecules and ions from experimentally determined structures to predicted protein models.

NATURE METHODS (2023)

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Article Multidisciplinary Sciences

Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2

Stefan Gahbauera et al.

Summary: This study reports the discovery and development of chemical scaffolds that bind to the nonstructural protein 3 (NSP3) of SARS-CoV-2. Through computational design and structural characterization, several potent ligands were identified, providing potential therapeutic options for COVID-19.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2023)

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Article Chemistry, Multidisciplinary

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

Feng Ren et al.

Summary: The application of artificial intelligence (AI) in drug discovery has revolutionized the field. The AlphaFold program's ability to predict protein structures for the entire human genome is considered a remarkable breakthrough. By applying AlphaFold, we successfully identified a novel hit molecule for a novel target without experimental structure.

CHEMICAL SCIENCE (2023)

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Article Biochemistry & Molecular Biology

Integrating structure-based approaches in generative molecular

Morgan Thomas et al.

Summary: Generative molecular design has the potential to improve the efficiency of drug discovery by exploring larger chemical spaces. This study focuses on incorporating protein structure into the optimization process to enhance binding affinity prediction. Different approaches are categorized based on whether protein structure is explicitly or implicitly considered in the generative model.

CURRENT OPINION IN STRUCTURAL BIOLOGY (2023)

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Article Chemistry, Medicinal

Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

Yuqi Zhang et al.

Summary: The AlphaFold2 (AF2) algorithm predicts proteins' 3D structures from amino acid sequences. The open AlphaFold protein structure database covers the complete human proteome. Through virtual screening, AF2 structures show comparable early enrichment of known active compounds to apo structures, and with refinement using an induced-fit protocol, the performance in structure-based virtual screening can be improved. Thus, AF2 structures hold promise for in silico hit identification.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023)

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Article Chemistry, Medicinal

Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs

Louis Bellmann et al.

Summary: In this study, a new tool called SpaceCompare is introduced for calculating the overlap of large chemical spaces. Unlike existing methods, SpaceCompare utilizes topological fingerprints and the combinatorial character of these chemical spaces, allowing for the accurate determination of the overlap of prominent spaces such as Enamine’s REAL Space, WuXi’s GalaXi Space, and Otava’s CHEMriya for the first time.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

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Article Chemistry, Medicinal

A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry

Yuliana Zabolotna et al.

Summary: Efficient synthesis of desired compounds is crucial for chemical space exploration in drug discovery, which is influenced by both established synthetic protocols and the availability of corresponding building blocks (BBs). This study analyzes the chemical space of 400,000 purchasable BBs, examining their physicochemical properties and diversity to assess their coverage of medicinal chemistry needs. The analysis is based on a universal topographic map that visualizes libraries and their differences in coverage.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

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Article Chemistry, Multidisciplinary

Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses

Andreas Luttens et al.

Summary: Developing drug inhibitors targeting SARS-CoV-2 is crucial for saving lives and preparing for future outbreaks. Two virtual screening strategies were explored, resulting in the identification of compounds with inhibitory effects, including one compound with promising antiviral activity.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2022)

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Article Multidisciplinary Sciences

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Arman A. Sadybekov et al.

Summary: Structure-based virtual ligand screening using V-SYNTHES method is effective in rapidly identifying high-scoring compounds from a large chemical space, with experimental results demonstrating successful hit rates and potencies. This approach shows promise for lead discovery and is easily scalable for use with diverse docking algorithms.

NATURE (2022)

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Article Multidisciplinary Sciences

Automated iterative Csp3-C bond formation

Daniel J. Blair et al.

Summary: Fully automated synthetic chemistry has the potential to revolutionize the field by providing on-demand access to small molecules. The development of a new class of stable TIDA boronates enables the automated stereospecific assembly of Csp(3)-C bonds, expanding the range of reactions that can be run autonomously. This breakthrough allows for the synthesis of increasingly complex Csp(3)-rich small molecules via automated assembly.

NATURE (2022)

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Article Biochemical Research Methods

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking

Francesco Gentile et al.

Summary: With the rapid expansion of chemical libraries, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform allows for accelerated structure-based virtual screening by docking only a subset of the library and synchronizing it with ligand-based predictions. This method enables the screening of large chemical libraries without requiring extraordinary computational resources.

NATURE PROTOCOLS (2022)

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Editorial Material Biotechnology & Applied Microbiology

AI in small-molecule drug discovery: a coming wave?

Madura K. P. Jayatunga et al.

NATURE REVIEWS DRUG DISCOVERY (2022)

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Article Multidisciplinary Sciences

Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits

Giacomo G. Rossetti et al.

Summary: A potential M-pro inhibitor for treating SARS-CoV-2 infections has been identified and its efficacy and mode of action have been further characterized.

SCIENTIFIC REPORTS (2022)

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Article Pharmacology & Pharmacy

Why 90 % of clinical drug development fails and how to improve it?

Duxin Sun et al.

Summary: Despite the implementation of many successful strategies, the majority of clinical drug development fails. This study suggests that current drug optimization strategies overlook tissue exposure/selectivity, leading to misleading drug candidate selection and impacting clinical efficacy/toxicity balance. The proposed structure-tissue exposure/selectivity-activity relationship (STAR) classification system aims to improve drug optimization and enhance the success rate of clinical drug development.

ACTA PHARMACEUTICA SINICA B (2022)

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Article Chemistry, Multidisciplinary

Digitizing Chemical Synthesis in 3D Printed Reactionware

Andrius Bubliauskas et al.

Summary: Chemistry digitization requires a standardized approach to link experiments with the code used to generate experimental conditions and outcomes. This study presents a new approach that combines process chemistry principles with 3D printed reactionware to digitize organic synthesis.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2022)

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Review Chemistry, Medicinal

Exploration of Ultralarge Compound Collections for Drug Discovery

Wendy A. Warr et al.

Summary: Designing new medicines more cheaply and quickly is closely related to exploring chemical space more widely and efficiently. This review focuses on the collection of millions or even billions of enumerated chemical structures, as well as larger chemical spaces that are not fully enumerated. New technologies for searching large libraries and combinatorially in chemical space are discussed, along with space navigation techniques and the impact of autonomous laboratories on synthesis of designed compounds. Challenges and opportunities for the future are summarized.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

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Editorial Material Chemistry, Medicinal

Special Issue on Reaction Informatics and Chemical Space

Matthias Rarey et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

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Article Chemistry, Medicinal

On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks

Mikhail Volkov et al.

Summary: The study suggests that explicitly describing protein-ligand noncovalent interactions does not provide an advantage over using ligand or protein descriptors. Simple models already exhibit good performances, indicating that memorization dominates true learning in deep neural networks.

JOURNAL OF MEDICINAL CHEMISTRY (2022)

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Article Chemistry, Medicinal

Defining Levels of Automated Chemical Design

Brian Goldman et al.

Summary: One area of computational methods in drug discovery is the automated design of small molecules. However, there is a lack of agreement on terminology and key attributes in this field. The introduction of Automated Chemical Design (ACD) Levels aims to define the level of autonomy in ideation and decision making, providing a common language for describing and evaluating such systems.

JOURNAL OF MEDICINAL CHEMISTRY (2022)

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Article Chemistry, Multidisciplinary

Machine Learning May Sometimes Simply Capture LiteraturePopularity Trends: A Case Study of Heterocyclic Suzuki-MiyauraCoupling

Wiktor Beker et al.

Summary: The application of machine learning in synthetic chemistry relies on the assumption that abundant literature examples can enable accurate and predictive models. However, this paper demonstrates that carefully curated literature data may not be sufficient for this purpose. Regardless of the machine learning model or representation used, machine learning methods fail to predict optimum reaction conditions better than naive assignments based on the frequency of certain conditions reported in the literature.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2022)

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Article Multidisciplinary Sciences

Ni-electrocatalytic Csp3-Csp3 doubly decarboxylative coupling

Benxiang Zhang et al.

Summary: “Cross-coupling reactions are powerful tools for rapidly assembling complex molecules, but their use is limited by traditional oxidative electrolytic protocols. This study demonstrates a mildly reductive Ni-electrocatalytic system that enables coupling of different carboxylates through in situ generation of redox-active esters. This simple and versatile method offers a powerful new approach for synthesis by heterocoupling various levels of redox-active esters.”

NATURE (2022)

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Review Chemistry, Multidisciplinary

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding

Suzanne Ackloo et al.

Summary: One goal of computational chemistry is to improve small-molecule hit-finding algorithms through prediction and experimental testing. CACHE is a public benchmarking project that evaluates and compares computational approaches, aiming to discover new small-molecule binders for important protein targets.

NATURE REVIEWS CHEMISTRY (2022)

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Article Biochemistry & Molecular Biology

A structural biology community assessment of AlphaFold2 applications

Mehmet Akdel et al.

Summary: This study evaluates the performance of AlphaFold2 in structural biology applications and finds that it performs well and can partially replace experimentally determined structures, which is of great significance for life science research.

NATURE STRUCTURAL & MOLECULAR BIOLOGY (2022)

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Article Biochemistry & Molecular Biology

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery

Felix Wong et al.

Summary: Efficient identification of drug mechanisms of action remains a challenge. In this study, AlphaFold2 combined with molecular docking simulations was used to predict protein-ligand interactions and revealed widespread compound and protein promiscuity. Rescoring docking poses using machine learning-based approaches improved model performance. This work highlights the need for further development of protein-ligand interaction modeling, particularly using machine learning-based approaches, to better harness AlphaFold2 for drug discovery.

MOLECULAR SYSTEMS BIOLOGY (2022)

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Article Multidisciplinary Sciences

Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity

Anat Levit Kaplan et al.

Summary: Researchers successfully screened and synthesized molecules that can activate the 5-HT2A receptor using structure-based docking and optimization methods. These molecules exhibited potent antidepressant activity in mouse models without psychedelic effects.

NATURE (2022)

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Article Multidisciplinary Sciences

Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors

Paul Beroza et al.

Summary: The authors present a computational strategy, Chemical Space Docking, which combines docking with a reaction-based search of compounds, enabling the exploration of large compound libraries for drug lead discovery. They successfully applied this method to identify inhibitors of ROCK1 and confirmed their findings with X-ray structures.

NATURE COMMUNICATIONS (2022)

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Review Computer Science, Artificial Intelligence

The transformational role of GPU computing and deep learning in drug discovery

Mohit Pandey et al.

Summary: This review summarizes the significant role GPUs have played in advancing deep learning methods in drug discovery, including their applications in accelerating molecular docking, evaluating off-target effects, and predicting pharmacological properties.

NATURE MACHINE INTELLIGENCE (2022)

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Article Biochemistry & Molecular Biology

Target 2035-update on the quest for a probe for every protein

Susanne Mueller et al.

Summary: Twenty years after the first draft of the human genome was published, our understanding of the human proteome is still incomplete. The majority of proteins in the human proteome remain uncharacterized, with only a small percentage successfully targeted for drug discovery. Target 2035 aims to bridge this gap by developing new technologies for the entire human proteome by 2035.

RSC MEDICINAL CHEMISTRY (2022)

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Review Chemistry, Multidisciplinary

DNA-Encoded Chemistry: Drug Discovery from a Few Good Reactions

Patrick R. Fitzgerald et al.

Summary: Click chemistry and DNA-encoded library (DEL) technology share many similarities in their design and deployment in combinatorial chemistry, providing frameworks for the development of new synthesis methods to drive next-generation drug discovery.

CHEMICAL REVIEWS (2021)

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Article

From computer-aided drug discovery to computer-driven drug discovery

Leah Frye et al.

Drug Discovery Today: Technologies (2021)

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Review Chemistry, Medicinal

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Victor T. Sabe et al.

Summary: Computer-aided drug design (CADD) is a pivotal approach in contemporary pre-clinical drug discovery, utilizing various computational techniques and software programs to achieve desired outcomes. Research evaluated the techniques and software programs used in CADD, along with experimental validations. The application of CADD has led to the discovery of numerous approved drugs, with a growing trend in the field.

EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY (2021)

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Article Biotechnology & Applied Microbiology

Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries

Miljan Kuljanin et al.

Summary: This study presents a redesigned workflow called streamlined cysteine activity-based protein profiling (SLC-ABPP) for measuring amino acid side-chain reactivity, which significantly improves sample throughput. The method has been successfully applied to identify proteome-wide targets of various covalent inhibitors, while also creating a resource of cysteine reactivity data for further research.

NATURE BIOTECHNOLOGY (2021)

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Article Chemistry, Physical

Efficient Exploration of Chemical Space with Docking and Deep Learning

Ying Yang et al.

Summary: The increasing availability of purchasable compounds for virtual screening and assay has led to the development of a machine learning-enhanced molecular docking protocol, which drastically improves throughput and preserves the diversity of experimentally confirmed hit compounds. This protocol successfully identifies high scoring compounds while exploring a large region of chemical space, demonstrating superior performance compared to traditional methods.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Article Multidisciplinary Sciences

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening

Christoph Gorgulla et al.

Summary: The global effort to combat the ongoing COVID-19 pandemic has led to promising prophylactic measures, but there is still a need for effective therapeutics. Through a large-scale virtual screening platform, inhibitors targeting SARS-CoV-2 are being searched for, focusing on both viral enzymes' active sites and critical protein-protein interactions.

ISCIENCE (2021)

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Article Multidisciplinary Sciences

Structures of the σ2, receptor enable docking for bioactive ligand discovery

Assaf Alon et al.

Summary: The study determined the crystal structure of sigma(2) receptor with roluperidone and PB28, identified new chemical compounds with higher affinities, and confirmed the role of sigma(2) receptor in pain perception, highlighting the potential for developing new pain treatments.

NATURE (2021)

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Article Biochemical Research Methods

A practical guide to large-scale docking

Brian J. Bender et al.

Summary: Structure-based docking screens of compound libraries are common in early drug and probe discovery. Best practices and control calculations are outlined to evaluate docking parameters prior to undertaking a large-scale prospective screen.

NATURE PROTOCOLS (2021)

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Review Pharmacology & Pharmacy

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

Flavio Ballante et al.

Summary: GPCRs constitute the largest family of membrane proteins in the human genome, and the increasing number of atomic-resolution structures has provided valuable insights for drug design. Structure-based virtual screening is an efficient computational approach to identify novel chemical probes from large compound libraries.

PHARMACOLOGICAL REVIEWS (2021)

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Article Biochemistry & Molecular Biology

Applying and improving AlphaFold at CASP14

John Jumper et al.

Summary: The AlphaFold system made significant improvements in CASP14, achieving a high level of accuracy in protein structure prediction and performing remarkably well on Free Modeling targets.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2021)

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Article Multidisciplinary Sciences

An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19

Dafydd R. Owen et al.

Summary: PF-07321332, an orally bioavailable SARS-CoV-2 main protease inhibitor, has been discovered with in vitro pan-human coronavirus antiviral activity and excellent off-target selectivity and in vivo safety profiles. This new drug has shown promise in countering the threat of COVID-19 with its oral activity and safety in clinical trials.

SCIENCE (2021)

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Review Biochemical Research Methods

Utilizing graph machine learning within drug discovery and development

Thomas Gaudelet et al.

Summary: Graph machine learning (GML) is gaining attention in the pharmaceutical and biotechnology industries for its ability to model biomolecular structures and integrate multi-omic datasets. While still emerging, milestones such as repurposed drugs entering in vivo studies indicate that GML will become a preferred modeling framework in biomedical machine learning.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Chemistry, Medicinal

Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

Derek Jones et al.

Summary: Accurately predicting protein-ligand binding affinities is crucial in drug discovery. While current methods face challenges, fusion models that combine features and inference from complementary representations show improved prediction accuracy. Comparative analysis reveals that fusion models perform better than individual neural network models, docking scoring, and MM/GBSA calculations, with the added benefit of greater computational efficiency.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

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Article Chemistry, Physical

Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding

Edward B. Miller et al.

Summary: This study presents a reliable and accurate solution for protein-ligand binding by combining different docking methods, achieving a low root-mean-square deviation in over 90% of cases. The predicted ligand-receptor structures were accurate enough to enable predictive structure-based drug discovery for challenging targets, expanding the applicability of such methods.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Article Multidisciplinary Sciences

Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

Jeongtae Son et al.

Summary: The study introduces a prediction model named GraphBAR based on graph convolutional neural networks for predicting protein-ligand binding affinity. Data augmentation and docking simulation data can improve the model performance.

PLOS ONE (2021)

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Article Multidisciplinary Sciences

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking

Marion Schuller et al.

Summary: A large-scale crystallographic screening and computational docking effort identified new chemical matter targeting the active site of the SARS-CoV-2 macrodomain, providing a starting point for the development of potent macro-domain inhibitors.

SCIENCE ADVANCES (2021)

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Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

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Article Multidisciplinary Sciences

Highly accurate protein structure prediction for the human proteome

Kathryn Tunyasuvunakool et al.

Summary: Using the AlphaFold method, the structural coverage of the proteome has been significantly expanded, covering 98.5% of human proteins with 58% of residues having confident predictions and 36% having very high confidence. Introducing new metrics to interpret the dataset and identify disordered regions, this study aims to provide high-quality predictions for generating biological hypotheses.

NATURE (2021)

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Article Multidisciplinary Sciences

Accurate prediction of protein structures and interactions using a three-track neural network

Minkyung Baek et al.

Summary: Through the three-track network, we achieved accuracies approaching those of DeepMind in CASP14, enabling rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and providing insights into the functions of proteins with currently unknown structure.

SCIENCE (2021)

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Article Multidisciplinary Sciences

Crowdsourced mapping of unexplored target space of kinase inhibitors

Anna Cichonska et al.

Summary: By benchmarking predictive algorithms on unpublished bioactivity data, it was found that ensemble models based on various approaches can improve prediction accuracy and accelerate experimental mapping of unexplored compound-kinase interactions.

NATURE COMMUNICATIONS (2021)

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Article Multidisciplinary Sciences

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Francesca Grisoni et al.

Summary: Automating the molecular design-make-test-analyze cycle has led to successful generation of potent LXR agonists, confirming the applicability of the proposed framework for automated drug design.

SCIENCE ADVANCES (2021)

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Article Computer Science, Artificial Intelligence

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Michael Roberts et al.

Summary: Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images. However, a systematic review found that current studies have methodological flaws, preventing their potential clinical utility. Recommendations are provided to address these issues for higher-quality model development.

NATURE MACHINE INTELLIGENCE (2021)

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Article Biochemical Research Methods

Affinity selection-mass spectrometry for the discovery of pharmacologically active compounds from combinatorial libraries and natural products

Ruth N. Muchiri et al.

Summary: Affinity selection-mass spectrometry (AS-MS) was invented to address the high-throughput screening demands of combinatorial chemistry, utilizing binding interactions between ligands and receptors to isolate pharmacologically active compounds from mixtures of small molecules. AS-MS has three main approaches: pulsed ultrafiltration (PUF) AS-MS, size exclusion chromatography (SEC) AS-MS, and magnetic microbead affinity selection screening (MagMASS), with the capacity to screen hundreds of thousands of compounds per day.

JOURNAL OF MASS SPECTROMETRY (2021)

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Article Chemistry, Multidisciplinary

Alchemical absolute protein-ligand binding free energies for drug design

Y. Khalak et al.

Summary: Recent advances in relative protein-ligand binding free energy calculations have demonstrated the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies remains a challenging task, especially when considering the need to explicitly account for the protein in its apo state. This study presents several approaches for obtaining apo state ensembles to facilitate accurate absolute Delta G calculations and outlines protocols for future application in drug discovery.

CHEMICAL SCIENCE (2021)

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Review Pharmacology & Pharmacy

Fragment-based drug discovery using cryo-EM

Michael Saur et al.

DRUG DISCOVERY TODAY (2020)

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Article Biochemistry & Molecular Biology

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

Conor D. Parks et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2020)

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Article Multidisciplinary Sciences

Virtual discovery of melatonin receptor ligands to modulate circadian rhythms

Reed M. Stein et al.

NATURE (2020)

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Review Biotechnology & Applied Microbiology

Rethinking drug design in the artificial intelligence era

Petra Schneider et al.

NATURE REVIEWS DRUG DISCOVERY (2020)

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Article Multidisciplinary Sciences

Veridical data science

Bin Yu et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

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Review Chemistry, Multidisciplinary

Next generation 3D pharmacophore modeling

David Schaller et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2020)

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Review Biochemistry & Molecular Biology

Impact of GPCR Structures on Drug Discovery

Miles Congreve et al.

CELL (2020)

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Review Chemistry, Multidisciplinary

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Lauro Ribeiro de Souza Neto et al.

FRONTIERS IN CHEMISTRY (2020)

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Article Multidisciplinary Sciences

Improving detection of protein-ligand binding sites with 3D segmentation

Marta M. Stepniewska-Dziubinska et al.

SCIENTIFIC REPORTS (2020)

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Article Chemistry, Medicinal

Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds

Anh-Tien Ton et al.

MOLECULAR INFORMATICS (2020)

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Article Multidisciplinary Sciences

An open-source drug discovery platform enables ultra-large virtual screens

Christoph Gorgulla et al.

NATURE (2020)

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Article Multidisciplinary Sciences

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

Zhenming Jin et al.

NATURE (2020)

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Review Pharmacology & Pharmacy

High-Throughput Screening: today's biochemical and cell-based approaches

Vincent Blay et al.

DRUG DISCOVERY TODAY (2020)

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Article Chemistry, Medicinal

Rigorous Free Energy Simulations in Virtual Screening

Zoe Cournia et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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Article Chemistry, Medicinal

Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding

Kevin McCloskey et al.

JOURNAL OF MEDICINAL CHEMISTRY (2020)

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Review Pharmacology & Pharmacy

Can easy chemistry produce complex, diverse, and novel molecules?

Anna Tomberg et al.

DRUG DISCOVERY TODAY (2020)

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Review Pharmacology & Pharmacy

Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades

Michael Davies et al.

TRENDS IN PHARMACOLOGICAL SCIENCES (2020)

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Article Chemistry, Medicinal

The Advent of Generative Chemistry

Quentin Vanhaelen et al.

ACS MEDICINAL CHEMISTRY LETTERS (2020)

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Article Chemistry, Medicinal

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Christina E. M. Schindler et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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