| scholarly article | Q13442814 |
| P50 | author | Isabella A Guedes | Q85971109 |
| P2093 | author name string | Laurent E Dardenne | |
| Felipe S S Pereira | |||
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| Recommendations for evaluation of computational methods | Q28755020 | ||
| Beware of q2! | Q28842863 | ||
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| Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions | Q30490868 | ||
| CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series | Q30543685 | ||
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| Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments | Q44012277 | ||
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| Kinase inhibitors and the case for CH...O hydrogen bonds in protein-ligand binding | Q44195807 | ||
| Comparative evaluation of 11 scoring functions for molecular docking | Q44457437 | ||
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| From machine learning to deep learning: progress in machine intelligence for rational drug discovery. | Q45945808 | ||
| Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening. | Q45946918 | ||
| D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies | Q46050747 | ||
| Interpreting steep dose-response curves in early inhibitor discovery | Q46152094 | ||
| Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes | Q46586108 | ||
| ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions | Q46690363 | ||
| Modeling water molecules in protein-ligand docking using GOLD. | Q46725683 | ||
| Ranking Power of the SQM/COSMO Scoring Function on Carbonic Anhydrase II-Inhibitor Complexes | Q47217749 | ||
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| Recent updates on Computer-aided drug discovery: Time for a Paradigm Shift | Q47237220 | ||
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| GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking | Q47565870 | ||
| Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort | Q47684449 | ||
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| Paying the Price of Desolvation in Solvent-Exposed Protein Pockets: Impact of Distal Solubilizing Groups on Affinity and Binding Thermodynamics in a Series of Thermolysin Inhibitors. | Q48014039 | ||
| Comparative assessment of scoring functions on a diverse test set. | Q48026800 | ||
| Importance of protein flexibility in molecular recognition: a case study on Type-I1/2 inhibitors of ALK. | Q48042996 | ||
| Decoys Selection in Benchmarking Datasets: Overview and Perspectives. | Q49221491 | ||
| SFCscore: scoring functions for affinity prediction of protein-ligand complexes. | Q50131043 | ||
| DOCK 6: Impact of new features and current docking performance | Q35748554 | ||
| Inexpensive Method for Selecting Receptor Structures for Virtual Screening. | Q35864805 | ||
| Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments | Q35892490 | ||
| CSM-lig: a web server for assessing and comparing protein-small molecule affinities. | Q36010254 | ||
| Variability in docking success rates due to dataset preparation | Q36095634 | ||
| Robust scoring functions for protein-ligand interactions with quantum chemical charge models | Q36264705 | ||
| The impact of in silico screening in the discovery of novel and safer drug candidates | Q36287029 | ||
| Water mediation in protein folding and molecular recognition | Q36475408 | ||
| Calculation of protein-ligand binding affinities. | Q36698596 | ||
| Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications | Q36903666 | ||
| Do enthalpy and entropy distinguish first in class from best in class? | Q36967464 | ||
| Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. | Q37014096 | ||
| Check your confidence: size really does matter | Q37291540 | ||
| The Problem of Overfitting | Q37414344 | ||
| Targeted scoring functions for virtual screening | Q37512717 | ||
| Lessons Learned over Four Benchmark Exercises from the Community Structure-Activity Resource | Q37565924 | ||
| Structure-based virtual ligand screening: recent success stories | Q37660412 | ||
| Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. | Q37781709 | ||
| The role of protonation states in ligand-receptor recognition and binding | Q38061449 | ||
| Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap | Q38071408 | ||
| Latest developments in molecular docking: 2010-2011 in review | Q38092807 | ||
| Theory and applications of covalent docking in drug discovery: merits and pitfalls | Q38336673 | ||
| In situ extension as an approach for identifying novel alpha-amylase inhibitors | Q38337897 | ||
| Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. | Q38390892 | ||
| Protein Flexibility in Drug Discovery: From Theory to Computation | Q38428430 | ||
| Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. | Q38566204 | ||
| Why and how have drug discovery strategies in pharma changed? What are the new mindsets? | Q38587946 | ||
| Understanding the challenges of protein flexibility in drug design. | Q38594557 | ||
| Computational Tools To Model Halogen Bonds in Medicinal Chemistry. | Q38606728 | ||
| Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2. | Q38613029 | ||
| Receptor-ligand molecular docking | Q38783140 | ||
| Empirical Scoring Functions for Affinity Prediction of Protein-ligand Complexes | Q38792709 | ||
| Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods. | Q38808168 | ||
| Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action | Q38820694 | ||
| Performance of machine-learning scoring functions in structure-based virtual screening | Q38821300 | ||
| Prospects of Applying Enhanced Semi-Empirical QM Methods for 2101 Virtual Drug Design | Q38835463 | ||
| Water, water, everywhere… It's time to stop and think | Q38841144 | ||
| Protein-Ligand Scoring with Convolutional Neural Networks. | Q38860867 | ||
| A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016. | Q38964411 | ||
| Boosting Docking-Based Virtual Screening with Deep Learning | Q39064005 | ||
| On-the-Fly QM/MM Docking with Attracting Cavities. | Q39094845 | ||
| Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest | Q39177512 | ||
| Innovation in the pharmaceutical industry: New estimates of R&D costs | Q39194484 | ||
| Software for molecular docking: a review. | Q39310583 | ||
| Modeling covalent-modifier drugs. | Q39320719 | ||
| D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions | Q39328873 | ||
| Covalent inhibitors design and discovery. | Q39398812 | ||
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| Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015. | Q39465346 | ||
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| The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. | Q40496139 | ||
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| Evaluating the potential of halogen bonding in molecular design: automated scaffold decoration using the new scoring function XBScore. | Q41475211 | ||
| A new class of models for computing receptor-ligand binding affinities | Q41504987 | ||
| Soft docking and multiple receptor conformations in virtual screening | Q41968000 | ||
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| Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging. | Q42202923 | ||
| Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking | Q42251037 | ||
| Ligand Identification Scoring Algorithm (LISA). | Q42288110 | ||
| A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking | Q42557837 | ||
| Divergent modes of enzyme inhibition in a homologous structure-activity series | Q42581674 | ||
| PDB-wide collection of binding data: current status of the PDBbind database | Q30858984 | ||
| Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest. | Q30971961 | ||
| DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction | Q31007675 | ||
| How does consensus scoring work for virtual library screening? An idealized computer experiment | Q31017163 | ||
| Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. | Q31033753 | ||
| Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets | Q31119539 | ||
| The computer program LUDI: a new method for the de novo design of enzyme inhibitors | Q33214112 | ||
| Consensus scoring criteria for improving enrichment in virtual screening | Q33220369 | ||
| GFscore: a general nonlinear consensus scoring function for high-throughput docking | Q33251333 | ||
| SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization | Q33410990 | ||
| Let's not forget tautomers | Q33511462 | ||
| Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles | Q33573726 | ||
| A Comprehensive Docking and MM/GBSA Rescoring Study of Ligand Recognition upon Binding Antithrombin. | Q33600404 | ||
| Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets. | Q33714943 | ||
| Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits | Q33757270 | ||
| Comments on "leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets": significance for the validation of scoring functions. | Q33904464 | ||
| AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins | Q34089389 | ||
| Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I). | Q34193676 | ||
| The experimental uncertainty of heterogeneous public K(i) data. | Q34285419 | ||
| The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure | Q34326042 | ||
| Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results | Q34414010 | ||
| Computer-based de novo design of drug-like molecules | Q34438895 | ||
| Further development and validation of empirical scoring functions for structure-based binding affinity prediction | Q34526566 | ||
| Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets | Q34738971 | ||
| Folding of chymotrypsin inhibitor 2. 1. Evidence for a two-state transition | Q34968585 | ||
| BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes | Q35141175 | ||
| Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. | Q35206344 | ||
| Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study | Q35232372 | ||
| Beware of machine learning-based scoring functions-on the danger of developing black boxes | Q35245699 | ||
| MTiOpenScreen: a web server for structure-based virtual screening. | Q35597960 | ||
| Ligand configurational entropy and protein binding | Q35607011 | ||
| Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2. | Q35615893 | ||
| Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives | Q35650804 | ||
| Computer-aided drug design: time to play with novel chemical matter. | Q50516673 | ||
| Implementing QM in docking calculations: is it a waste of computational time? | Q50540449 | ||
| Classification of current scoring functions. | Q50603070 | ||
| DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. | Q50989579 | ||
| Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease. | Q51044859 | ||
| Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening. | Q51645005 | ||
| Editorial: special issue on "Evaluation of computational methods". | Q51891050 | ||
| Supervised consensus scoring for docking and virtual screening. | Q51994661 | ||
| Identification of protein-protein interaction sites from docking energy landscapes. | Q52005983 | ||
| Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. | Q52069951 | ||
| Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. | Q52132563 | ||
| Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. | Q52135209 | ||
| Flexible protein-ligand docking by global energy optimization in internal coordinates. | Q52281044 | ||
| A fast flexible docking method using an incremental construction algorithm. | Q52298930 | ||
| The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. | Q52656049 | ||
| Assessing protein-ligand interaction scoring functions with the CASF-2013 benchmark. | Q52666970 | ||
| Virtual screening using protein-ligand docking: avoiding artificial enrichment. | Q53639582 | ||
| Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. | Q53678858 | ||
| Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry. | Q53825273 | ||
| Practices in Molecular Docking and Structure-Based Virtual Screening. | Q54118796 | ||
| Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization. | Q55016340 | ||
| The Taxonomy of Covalent Inhibitors | Q56675654 | ||
| Structure based virtual screening to discover putative drug candidates: Necessary considerations and successful case studies | Q56854167 | ||
| Entropy calculations on the molten globule state of a protein: Side-chain entropies of α-lactalbumin | Q57204703 | ||
| Free enthalpies of replacing water molecules in protein binding pockets | Q57235415 | ||
| NAMD goes quantum: an integrative suite for hybrid simulations | Q58009242 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | pharmacology | Q128406 |
| virtual screening | Q4112105 | ||
| P304 | page(s) | 1089 | |
| P577 | publication date | 2018-09-24 | |
| P1433 | published in | Frontiers in Pharmacology | Q2681208 |
| P1476 | title | Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges | |
| P478 | volume | 9 |
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