| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/ploscb/NevesBALCMCA20 |
| P356 | DOI | 10.1371/JOURNAL.PCBI.1007025 |
| P932 | PMC publication ID | 7048302 |
| P698 | PubMed publication ID | 32069285 |
| P50 | author | Carolina Horta Andrade | Q20047953 |
| Eugene Muratov | Q29460334 | ||
| Rodolpho C. Braga | Q53480128 | ||
| Bruno J. Neves | Q55762390 | ||
| Vinicius Alves | Q56514156 | ||
| Marilia N N Lima | Q88113670 | ||
| Fabio T M Costa | Q89759221 | ||
| P2093 | author name string | Gustavo C Cassiano | |
| P2860 | cites work | Spread of artemisinin resistance in Plasmodium falciparum malaria | Q21032481 |
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| Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants | Q27008504 | ||
| Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings | Q27861111 | ||
| Human malaria parasites in continuous culture | Q28131626 | ||
| QSAR modeling: where have you been? Where are you going to? | Q28222668 | ||
| ChEMBL: a large-scale bioactivity database for drug discovery | Q28315179 | ||
| Resistance mutations reveal the atovaquone-binding domain of cytochrome b in malaria parasites | Q28370808 | ||
| Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds | Q28386501 | ||
| New developments in anti-malarial target candidate and product profiles | Q28468598 | ||
| Best Practices for QSAR Model Development, Validation, and Exploitation | Q28649930 | ||
| Open-source platform to benchmark fingerprints for ligand-based virtual screening | Q28681290 | ||
| Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research | Q28748220 | ||
| The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service | Q28837925 | ||
| Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings | Q28842973 | ||
| Synchronization of Plasmodium falciparum Erythrocytic Stages in Culture | Q29547549 | ||
| Extended-connectivity fingerprints | Q29616639 | ||
| Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52 | Q30066516 | ||
| Deep learning for computational chemistry | Q30399624 | ||
| Genetic optimization of combinatorial libraries | Q30629341 | ||
| Robust QSAR models using Bayesian regularized neural networks. | Q52207019 | ||
| QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities. | Q52645960 | ||
| Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. | Q52659131 | ||
| Quantitative structure-activity relationships. VI. Non-linear dependence of biological activity on hydrophobic character: calculation procedures for bilinear model | Q52802390 | ||
| ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors | Q54376731 | ||
| Malaria | Q56378965 | ||
| Malaria | Q56383949 | ||
| Deep reinforcement learning for de novo drug design | Q57425404 | ||
| Improving Chemical Autoencoder Latent Space and Molecular Generation Diversity with Heteroencoders | Q58093222 | ||
| Comments on the Definition of theQ2Parameter for QSAR Validation | Q58627563 | ||
| Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks | Q59405112 | ||
| Hierarchical QSAR technology based on the Simplex representation of molecular structure | Q80642889 | ||
| Making big sense from big data in toxicology by read-across | Q31065842 | ||
| Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation | Q31106801 | ||
| Rational selection of training and test sets for the development of validated QSAR models | Q31165909 | ||
| Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules? | Q32032942 | ||
| Predictive QSAR modeling workflow, model applicability domains, and virtual screening | Q33316534 | ||
| Failure of artesunate-mefloquine combination therapy for uncomplicated Plasmodium falciparum malaria in southern Cambodia | Q33399259 | ||
| Allelic exchange at the endogenous genomic locus in Plasmodium falciparum proves the role of dihydropteroate synthase in sulfadoxine-resistant malaria | Q33889085 | ||
| Chloroquine-resistant malaria | Q34087800 | ||
| PubChem's BioAssay Database | Q34090643 | ||
| Molecular properties that influence the oral bioavailability of drug candidates | Q34130727 | ||
| Tuning HERG out: antitarget QSAR models for drug development | Q35164442 | ||
| Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization | Q35533343 | ||
| Reduced artemisinin susceptibility of Plasmodium falciparum ring stages in western Cambodia | Q36558472 | ||
| A broad analysis of resistance development in the malaria parasite | Q37017711 | ||
| QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni | Q37616239 | ||
| Transformation of Plasmodium falciparum malaria parasites by homologous integration of plasmids that confer resistance to pyrimethamine | Q37731639 | ||
| Virtual screening strategies in medicinal chemistry: the state of the art and current challenges. | Q38254964 | ||
| Is Multitask Deep Learning Practical for Pharma? | Q38429996 | ||
| Hit and lead criteria in drug discovery for infectious diseases of the developing world | Q38599011 | ||
| QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity | Q38743096 | ||
| The Next Era: Deep Learning in Pharmaceutical Research | Q38823933 | ||
| Severe malaria: what's new on the pathogenesis front? | Q38964820 | ||
| Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening | Q39300204 | ||
| Colloidal Aggregation Affects the Efficacy of Anticancer Drugs in Cell Culture | Q39342880 | ||
| Deep neural nets as a method for quantitative structure-activity relationships | Q41506565 | ||
| An Aggregation Advisor for Ligand Discovery. | Q41831853 | ||
| Curation of chemogenomics data | Q44858653 | ||
| Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression. | Q45964325 | ||
| Random forest: a classification and regression tool for compound classification and QSAR modeling | Q47370701 | ||
| Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins | Q47746759 | ||
| Automating drug discovery | Q47780833 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 2 | |
| P921 | main subject | malaria | Q12156 |
| deep learning | Q197536 | ||
| drug discovery | Q1418791 | ||
| P304 | page(s) | e1007025 | |
| P577 | publication date | 2020-02-18 | |
| P1433 | published in | PLOS Computational Biology | Q2635829 |
| P1476 | title | Deep Learning-driven research for drug discovery: Tackling Malaria | |
| P478 | volume | 16 |
| Q99632403 | Activity prediction of aminoquinoline drugs based on deep learning |
| Q103031650 | Artificial intelligence applied for the rapid identification of new antimalarial candidates with dual-stage activity |
| Q101468946 | Cancer classification based on chromatin accessibility profiles with deep adversarial learning model |
| Q108811449 | Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases |
| Q104566286 | Recent advances in Quantitative Structure-Activity Relationship models of antimalarial drugs |
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