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Enabling future drug discovery by de novo design
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Structure-based ligand design by dynamically assembling molecular building blocks at binding site
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The art and practice of structure-based drug design: A molecular modeling perspective
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“Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening
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Virtual Screening for Bioactive Molecules by Evolutionary De Novo Design
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Lead Generation Approaches in Drug Discovery
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High‐Throughput Screening and Virtual Screening: Entry Points to Drug Discovery
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FlexNovo : Structure‐Based Searching in Large Fragment Spaces
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From Molecular Shape to Potent Bioactive Agents I: Bioisosteric Replacement of Molecular Fragments
inferredcites →https://doi.org/10.1002/cmdc.200800314openalex-snapshot
From Molecular Shape to Potent Bioactive Agents II: Fragment‐Based de novo Design
inferredcites →https://doi.org/10.1002/jcc.540141008openalex-snapshot
CONCEPTS: New dynamic algorithm for de novo drug suggestion
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A novel computational tool for automated structure‐based drug design
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Pharmacophores in Drug Research
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Functionality maps of binding sites: A multiple copy simultaneous search method
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HOOK: A program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site
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A Fast Flexible Docking Method using an Incremental Construction Algorithm
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Knowledge-based scoring function to predict protein-ligand interactions
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Adaptive Combinatorial Design of Focused Compound Libraries
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PRO_LIGAND: An approach to de novo molecular design. 1. Application to the design of organic molecules
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PRO_LIGAND: An approach to de novo molecular design. 6. Flexible fitting in the design of peptides
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The computer program LUDI: A new method for the de novo design of enzyme inhibitors
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PRO_LIGAND: An approach to de novo molecular design. 3. A genetic algorithm for structure refinement
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A genetic algorithm for the automated generation of molecules within constraints
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PRO_LIGAND: An approach to de novo molecular design. 4. Application to the design of peptides
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SPLICE: A program to assemble partial query solutions from three-dimensional database searches into novel ligands
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LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads
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SPROUT: A program for structure generation
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The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure
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SPROUT, HIPPO and CAESA: Tools for de novo structure generation and estimation of synthetic accessibility
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LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design
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The de novo design of median molecules within a property range of interest
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A very large diversity space of synthetically accessible compounds for use with drug design programs
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Structure and reaction based evaluation of synthetic accessibility
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Automated site-directed drug design using molecular lattices
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Automated structure design in 3D
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The use of ligand-based de novo design for scaffold hopping and sidechain optimization: Two case studies
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Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase
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Discovery of a novel HCV helicase inhibitor by a de novo drug design approach
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Streamlining lead discovery by aligning in silico and high-throughput screening
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Virtual screening strategies in drug discovery
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Novel trends in high-throughput screening
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Virtual ligand screening: strategies, perspectives and limitations
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PhDD: A new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility
inferredcites →https://doi.org/10.1016/s1359-6446(01)02091-8openalex-snapshot
Virtual screening and fast automated docking methods
inferredcites →https://doi.org/10.1016/s1359-6446(01)02131-6openalex-snapshot
Target validation of G-protein coupled receptors
inferredcites →https://doi.org/10.1021/ci00017a027openalex-snapshot
SPROUT: Recent developments in the de novo design of molecules
inferredcites →https://doi.org/10.1021/ci00025a016openalex-snapshot
SPROUT: 3D Structure Generation Using Templates
inferredcites →https://doi.org/10.1021/ci00027a020openalex-snapshot
PRO_LIGAND: An Approach to de Novo Molecular Design. 5. Tools for the Analysis of Generated Structures
inferredcites →https://doi.org/10.1021/ci010375jopenalex-snapshot
Combinatorial Library Design Using a Multiobjective Genetic Algorithm
inferredcites →https://doi.org/10.1021/ci025634zopenalex-snapshot
Neighborhood Behavior of in Silico Structural Spaces with Respect to in Vitro Activity Spaces−A Novel Understanding of the Molecular Similarity Principle in the Context of Multiple Receptor Binding Profiles
inferredcites →https://doi.org/10.1021/ci034290popenalex-snapshot
A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules
inferredcites →https://doi.org/10.1021/ci0502956openalex-snapshot
Generation and Selection of Novel Estrogen Receptor Ligands Using the De Novo Structure-Based Design Tool, SkelGen
inferredcites →https://doi.org/10.1021/ci0503560openalex-snapshot
Flux (1): A Virtual Synthesis Scheme for Fragment-Based de Novo Design
inferredcites →https://doi.org/10.1021/ci060094hopenalex-snapshot
Recore: A Fast and Versatile Method for Scaffold Hopping Based on Small Molecule Crystal Structure Conformations
inferredcites →https://doi.org/10.1021/ci060117sopenalex-snapshot
On Outliers and Activity CliffsWhy QSAR Often Disappoints
inferredcites →https://doi.org/10.1021/ci100080ropenalex-snapshot
NovoFLAP: A Ligand-Based De Novo Design Approach for the Generation of Medicinally Relevant Ideas
inferredcites →https://doi.org/10.1021/ci6005307openalex-snapshot
Flux (2): Comparison of Molecular Mutation and Crossover Operators for Ligand-Based de Novo Design
inferredcites →https://doi.org/10.1021/ci700282uopenalex-snapshot
De Novo Ligand Design to Partially Flexible Active Sites: Application of the ReFlex Algorithm to Carboxypeptidase A, Acetylcholinesterase, and the Estrogen Receptor
inferredcites →https://doi.org/10.1021/ci700424bopenalex-snapshot
Fragment-Based de Novo Ligand Design by Multiobjective Evolutionary Optimization
inferredcites →https://doi.org/10.1021/ci800228yopenalex-snapshot
Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation
inferredcites →https://doi.org/10.1021/ci800272aopenalex-snapshot
Searching Fragment Spaces with Feature Trees
inferredcites →https://doi.org/10.1021/ci800308hopenalex-snapshot
De Novo Drug Design Using Multiobjective Evolutionary Graphs
inferredcites →https://doi.org/10.1021/ci8003094openalex-snapshot
Computational Fragment-Based Approach at PDB Scale by Protein Local Similarity
inferredcites →https://doi.org/10.1021/ci800413mopenalex-snapshot
Knowledge-Based Approach to de Novo Design Using Reaction Vectors
inferredcites →https://doi.org/10.1021/ci8004572openalex-snapshot
Fragment Shuffling: An Automated Workflow for Three-Dimensional Fragment-Based Ligand Design
inferredcites →https://doi.org/10.1021/ci9000458openalex-snapshot
FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules Occupying Druglike Chemical Space
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A Novel In Silico Approach to Drug Discovery via Computational Intelligence
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GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm
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RASSE: A New Method for Structure-Based Drug Design
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RECAPRetrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry
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Multiple Highly Diverse Structures Complementary to Enzyme Binding Sites: Results of Extensive Application of a de Novo Design Method Incorporating Combinatorial Growth
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Search for Improved Host Architectures: Application of de Novo Structure-Based Design and High-Throughput Screening Methods To Identify Optimal Building Blocks for Multidentate Ethers
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SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates. 1. Methodology and Supporting Evidence
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PRO_LIGAND: An Approach to de Novo Molecular Design. 2. Design of Novel Molecules from Molecular Field Analysis (MFA) Models and Pharmacophores
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GroupBuild: a fragment-based method for de novo drug design
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Multiple copy simultaneous search and construction of ligands in binding sites: application to inhibitors of HIV-1 aspartic proteinase
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The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses
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SMall Molecule Growth 2001 (SMoG2001): An Improved Knowledge-Based Scoring Function for Protein−Ligand Interactions
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BREED: Generating Novel Inhibitors through Hybridization of Known Ligands. Application to CDK2, P38, and HIV Protease
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SYNOPSIS: SYNthesize and OPtimize System in Silico
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Scaffold Hopping in De Novo Design. Ligand Generation in the Absence of Receptor Information
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LEA3D: A Computer-Aided Ligand Design for Structure-Based Drug Design
inferredcites →https://doi.org/10.1021/jm050226iopenalex-snapshot
Differential Response of Estrogen Receptor Subtypes to 1,3-Diarylindene and 2,3-Diarylindene Ligands
inferredcites →https://doi.org/10.1021/jm0613121openalex-snapshot
De Novo Parallel Design, Synthesis and Evaluation of Inhibitors against the Reverse Transcriptase of Human Immunodeficiency Virus Type-1 and Drug-Resistant Variants
inferredcites →https://doi.org/10.1021/jm0705713openalex-snapshot
SAR Index: Quantifying the Nature of Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/jm950792lopenalex-snapshot
CONCERTS: Dynamic Connection of Fragments as an Approach to de Novo Ligand Design
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A branch-and-bound method for optimal atom-type assignment in de novo ligand design
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Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
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Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs
inferredcites →https://doi.org/10.1023/a:1008042711516openalex-snapshot
Evaluation of a method for controlling molecular scaffold diversity in de novo ligand design
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PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology
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A genetic algorithm for the automated generation of small organic molecules: Drug design using an evolutionary algorithm
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De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks
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Predicting binding modes, binding affinities and `hot spots' for protein-ligand complexes using a knowledge-based scoring function
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Molecular Evolution: Automated Manipulation of Hierarchical Chemical Topology and Its Application to Average Molecular Structures
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Design of new selective inhibitors of cyclooxygenase-2 by dynamic assembly of molecular building blocks
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A genetic algorithm for structure-based de novo design
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Structure-based ligand design for flexible proteins: Application of new F-DycoBlock
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A validation study on the practical use of automated de novo design
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Development and testing of a de novo drug-design algorithm
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Computer-based de novo design of drug-like molecules
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Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening
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Equation of State Calculations by Fast Computing Machines
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Combinatorial computational method gives new picomolar ligands for a known enzyme
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Predicting resistance mutations using protein design algorithms
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Automatic molecular design using evolutionary techniques
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Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference
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Automated site-directed drug design: a general algorithm for knowledge acquisition about hydrogen-bonding regions at protein surfaces
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Automated site-directed drug design: the concept of spacer skeletons for primary structure generation
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Automated site-directed drug design : the formation of molecular templates in primary structure generation
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A New Q-Learning Algorithm Based on the Metropolis Criterion
inferredcites →https://doi.org/10.1111/j.1747-0285.2008.00672.xopenalex-snapshot
Concept of Combinatorial De Novo Design of Drug‐like Molecules by Particle Swarm Optimization
inferredcites →https://doi.org/10.1111/j.1747-0285.2008.00761.xopenalex-snapshot
AutoGrow: A Novel Algorithm for Protein Inhibitor Design
inferredcites →https://doi.org/10.1126/science.1150577openalex-snapshot
High-Resolution Crystal Structure of an Engineered Human β 2 -Adrenergic G Protein–Coupled Receptor
inferredcites →https://doi.org/10.1517/17460441.2010.497534openalex-snapshot
De novodesign: balancing novelty and confined chemical space
inferredcites →https://doi.org/10.2174/138161210791164009openalex-snapshot
Concepts and Applications of “Natural Computing” Techniques in De Novo Drug and Peptide Design
inferredcites →https://doi.org/10.2174/156802609789207091openalex-snapshot
Docking Screens: Right for the Right Reasons?
inferredcites →https://doi.org/10.2174/156802610790232305openalex-snapshot
Computational Approaches for Fragment-Based and De Novo Design
inferredcites →https://doi.org/10.3109/10799899709036621openalex-snapshot
Energetic and Entropic Factors Determining Binding Affinity in Protein-Ligand Complexes
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Molecular Design. Concepts and Applications
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Reaction-Driven De Novo Design, Synthesis and Testing of Potential Type II Kinase Inhibitors
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Artificial Intelligence and Machine Learning‐Based New Drug Discovery Process with Molecular Modelling
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MultiobjectiveDe NovoDesign of Synthetically Accessible Compounds
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Free Energy Methods in Ligand Design
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Hit and Lead Identification from Fragments
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Pharmacophore‐Based De Novo Design
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Predictive Methods in Lead Generation
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Future Perspectives of Computational Drug Design
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Immunosuppressive Small Molecule Discovered by Structure‐Based Virtual Screening for Inhibitors of Protein–Protein Interactions
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Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01385openalex-snapshot
Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02019openalex-snapshot
Transformer Decoder Learns from a Pretrained Protein Language Model to Generate Ligands with High Affinity
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00259openalex-snapshot
3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00839openalex-snapshot
GuacaMol: Benchmarking Models for de Novo Molecular Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00367openalex-snapshot
Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b00886openalex-snapshot
OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c00512openalex-snapshot
AI-Driven De Novo Design and Development of Nontoxic DYRK1A Inhibitors
inferred← citeshttps://doi.org/10.1021/acscatal.9b04952openalex-snapshot
Automated in Silico Design of Homogeneous Catalysts
inferred← citeshttps://doi.org/10.1021/acscentsci.7b00512openalex-snapshot
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1021/ja300865uopenalex-snapshot
An Evolutionary Algorithm for de Novo Optimization of Functional Transition Metal Compounds
inferred← citeshttps://doi.org/10.1038/nature25978openalex-snapshot
Planning chemical syntheses with deep neural networks and symbolic AI
inferred← citeshttps://doi.org/10.1038/s42256-019-0067-7openalex-snapshot
Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis
inferred← citeshttps://doi.org/10.1039/d1md00074hopenalex-snapshot
De novo molecular drug design benchmarking
inferred← citeshttps://doi.org/10.1039/d1ra03086hopenalex-snapshot
Transmol: repurposing a language model for molecular generation
inferred← citeshttps://doi.org/10.1039/d2dd00003bopenalex-snapshot
Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design
inferred← citeshttps://doi.org/10.1039/d3sc05306gopenalex-snapshot
Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network
inferred← citeshttps://doi.org/10.1080/17460441.2019.1615435openalex-snapshot
Approaching Target Selectivity by De Novo Drug Design
inferred← citeshttps://doi.org/10.1098/rsif.2017.0387openalex-snapshot
Opportunities and obstacles for deep learning in biology and medicine
inferred← citeshttps://doi.org/10.1186/s13321-018-0286-7openalex-snapshot
Molecular generative model based on conditional variational autoencoder for de novo molecular design
inferred← citeshttps://doi.org/10.1186/s13321-020-00431-wopenalex-snapshot
CReM: chemically reasonable mutations framework for structure generation
inferred← citeshttps://doi.org/10.1186/s13321-021-00501-7openalex-snapshot
MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES
inferred← citeshttps://doi.org/10.1186/s13321-021-00556-6openalex-snapshot
Individual and collective human intelligence in drug design: evaluating the search strategy
inferred← citeshttps://doi.org/10.1186/s13321-022-00638-zopenalex-snapshot
From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
inferred← citeshttps://doi.org/10.1517/17460441.2014.913565openalex-snapshot
Advances in multiparameter optimization methods for de novo drug design
inferred← citeshttps://doi.org/10.26434/chemrxiv-2021-x5sj7openalex-snapshot
National Institutes of Health (NIH) Workshop on Reaction Informatics
inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-090pfopenalex-snapshot
Src kinase app: valid inhibitor generation and prediction with explanation using predictive model and selfies
inferred← citeshttps://doi.org/10.3389/fimmu.2024.1438030openalex-snapshot
Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines
inferred← citeshttps://doi.org/10.3390/biom14070775openalex-snapshot
De Novo Design of Inhibitors of DNA Methyltransferase 1: A Critical Comparison of Ligand- and Structure-Based Approaches
inferred← citeshttps://doi.org/10.3390/molecules23102520openalex-snapshot
Artificial Intelligence in Drug Design
inferred← citeshttps://doi.org/10.3390/ph4091236openalex-snapshot
Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library
inferred← citeshttps://doi.org/10.4018/979-8-3693-9725-1.ch006openalex-snapshot
AI-Assisted Vaccine Design and Development in Immunological Disorders
inferred← citeshttps://doi.org/10.48550/arxiv.2012.11522openalex-snapshot
Barking up the right tree: an approach to search over molecule synthesis DAGs
inferred← citeshttps://doi.org/10.6084/m9.figshare.12178002openalex-snapshot
Additional file 1 of CReM: chemically reasonable mutations framework for structure generation
inferred← citeshttps://doi.org/10.6084/m9.figshare.14247554openalex-snapshot
Additional file 1 of MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES
inferred← citeshttps://doi.org/10.6084/m9.figshare.16786978openalex-snapshot
Additional file 1 of Individual and collective human intelligence in drug design: evaluating the search strategy
inferred← citeshttps://doi.org/10.6084/m9.figshare.20887214openalex-snapshot
Additional file 1 of From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
inferred← citeshttps://doi.org/10.63028/10067/2051650151162165141openalex-snapshot
Computational design of synthesizable molecules by imitating reference chemistry
inferred← citeshttps://doi.org/10.70675/d040b5b4zd926z4f6ez9870z7af586f801abopenalex-snapshot
Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments
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