Pages that link to "Q28842808"
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The following pages link to Molecular identification number for substructure searches (Q28842808):
Displaying 50 items.
- Recent Developments of the Chemistry Development Kit (CDK) - An Open-Source Java Library for Chemo- and Bioinformatics (Q27065423) (← links)
- Approaches to Measure Chemical Similarity– a Review (Q28531485) (← links)
- Identification of semicarbazones, thiosemicarbazones and triazine nitriles as inhibitors of Leishmania mexicana cysteine protease CPB (Q28534433) (← links)
- On Highly Discriminating Molecular Topological Index (Q28837994) (← links)
- A widely applicable set of descriptors (Q30643759) (← links)
- DISE: directed sphere exclusion (Q30883327) (← links)
- A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. (Q30903074) (← links)
- PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation (Q30986371) (← links)
- Combinatorial informatics in the post-genomics ERA. (Q31096793) (← links)
- Data mining a small molecule drug screening representative subset from NIH PubChem (Q31147766) (← links)
- A new method to estimate ligand-receptor energetics (Q33185485) (← links)
- Selecting compounds for focused screening using linear discriminant analysis and artificial neural networks (Q33203522) (← links)
- Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology (Q33282498) (← links)
- Statistical confidence for variable selection in QSAR models via Monte Carlo cross-validation (Q33317346) (← links)
- Synthesis, biological assays and QSAR studies of N-(9-benzyl-2-phenyl-8-azapurin-6-yl)-amides as ligands for A1 adenosine receptors (Q33410933) (← links)
- A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition (Q33452821) (← links)
- The T1R2/T1R3 sweet receptor and TRPM5 ion channel taste targets with therapeutic potential (Q33651965) (← links)
- Compound Acquisition and Prioritization Algorithm for Constructing Structurally Diverse Compound Libraries (Q33867931) (← links)
- Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem (Q33913583) (← links)
- Indexing molecules with chemical graph identifiers (Q33925477) (← links)
- An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential (Q34080242) (← links)
- Identification of lead compounds targeting the cathepsin B-like enzyme of Eimeria tenella (Q34092125) (← links)
- Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds (Q34992419) (← links)
- Screening of a chemical library reveals novel PXR-activating pharmacologic compounds (Q35470873) (← links)
- In Silico Prediction of Caco-2 Cell Permeability by a Classification QSAR Approach (Q36088290) (← links)
- Bayesian regularization of neural networks (Q37344410) (← links)
- Recent advances in fragment-based QSAR and multi-dimensional QSAR methods (Q37820219) (← links)
- CKB - the compound knowledge base: a text based chemical search system (Q38431461) (← links)
- 2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma (Q38441539) (← links)
- QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3D-MoRSE descriptors and statistical considerations about variable selection (Q38443118) (← links)
- Predicting the Absorption Potential of Chemical Compounds through a Deep Learning Approach (Q38789863) (← links)
- Exploiting Multiple Descriptor Sets in QSAR Studies (Q38791649) (← links)
- Optimizing the macrocyclic diterpenic core toward the reversal of multidrug resistance in cancer (Q38877337) (← links)
- Knowledge-based algorithms for chemical structure and property analysis (Q40246246) (← links)
- Cytotoxicity of cis-platinum(II) conjugate models. The effect of chelating arms and leaving groups on cytotoxicity: a quantitative structure-activity relationship approach. (Q40462341) (← links)
- Drug discovery: Past, present and future (Q40850718) (← links)
- Binary formal inference-based recursive modeling using multiple atom and physicochemical property class pair and torsion descriptors as decision criteria (Q40874879) (← links)
- QSAR models for bioconcentration: is the increase in the complexity justified by more accurate predictions? (Q41372949) (← links)
- In-silico predictive mutagenicity model generation using supervised learning approaches (Q42088046) (← links)
- Automated identification of crystallographic ligands using sparse-density representations. (Q42111041) (← links)
- Prediction of PKCθ inhibitory activity using the Random Forest Algorithm (Q42409150) (← links)
- Mining the National Cancer Institute's tumor-screening database: identification of compounds with similar cellular activities (Q42669490) (← links)
- Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network (Q42823140) (← links)
- Substituted hydrazinecarbothioamide as potent antitubercular agents: synthesis and quantitative structure-activity relationship (QSAR). (Q43122953) (← links)
- Virtual screen for ligands of orphan G protein-coupled receptors (Q44096715) (← links)
- Quantitative structure-activity relationships of mosquito larvicidal chalcone derivatives (Q44161703) (← links)
- The Compressed Feature Matrix--a novel descriptor for adaptive similarity search (Q44362552) (← links)
- A factorial design to optimize cell-based drug discovery analysis (Q44856105) (← links)
- Generation of QSAR sets with a self-organizing map. (Q45031845) (← links)
- A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). (Q45083599) (← links)