The Experts below are selected from a list of 6750 Experts worldwide ranked by ideXlab platform
Navid R. Moheimani - One of the best experts on this subject based on the ideXlab platform.
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microalgae cultivation for the treatment of anaerobically digested municipal centrate admc and anaerobically digested abattoir effluent adae
Science of The Total Environment, 2021Co-Authors: Ashiwin Vadiveloo, Louise Foster, Cherono Kwambai, Parisa A Bahri, Navid R. MoheimaniAbstract:Abstract The successful cultivation of microalgae in wastewater establishes a waste to profit scenario as it combines treatment of a waste stream with production of valuable end-products. Here, growth and nutrient removal efficiency of three different locally isolated microalgal cultures (Chlorella sp., Scenedesmus sp., and a mixed consortium) cultivated in anaerobically digested municipal centrate (ADMC) and anaerobically digested abattoir effluent (ADAE) was evaluated. No significant differences (P > 0.05) in specific growth rate and biomass productivity were recorded between Chlorella monocultures and the mixed culture grown in both effluents. Scenedesmus sp. monocultures was found incapable of growth in both ADMC and ADAE throughout the cultivation period resulting in the collapse of cultures and no further measurements on the growth, biomass production and nutrient removal efficiency of this alga in both effluent. Fq´/Fm´ values which represent the immediate photo-physiological status of microalgae found to be negatively inhibited when Scenedesmus sp. was grown in both effluents throughout the cultivation period. Fq´/Fm´ values of Chlorella sp. monocultures and the mixed cultures recovered back to normal (≈0.6) after an initial drop. Ammonium removal rates was found to be significantly higher (≈2 folds) for Chlorella sp. monocultures grown in both ADMC and ADAE when compared to the mixed cultures. Nonetheless, no significant differences were observed in the removal of phosphate for both cultures in the different effluents. The total protein and carbohydrate content of the biomass produced was similar for both microalgae cultures grown using ADAE and ADMC. However, chlorophyll a and total carotenoids content were found to be higher (P
Friedemann K Schmidt - One of the best experts on this subject based on the ideXlab platform.
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predictive multitask deep neural network models for ADME tox properties learning from large data sets
Journal of Chemical Information and Modeling, 2019Co-Authors: Jan Wenzel, Hans Matter, Friedemann K SchmidtAbstract:Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity) databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyze those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends among multiple ADME-Tox parameters for implicitly correlated data sets. Here we describe a novel, fully industrialized approach to parametrize and optimize the setup, training, application, and visual interpretation of DNNs to model ADME-Tox data. Investigated properties include microsomal lability in different species, passive permeability in Caco-2/TC7 cells, and logD. Statistical models are developed using up to 50 000 compounds from pub...
Ashiwin Vadiveloo - One of the best experts on this subject based on the ideXlab platform.
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microalgae cultivation for the treatment of anaerobically digested municipal centrate admc and anaerobically digested abattoir effluent adae
Science of The Total Environment, 2021Co-Authors: Ashiwin Vadiveloo, Louise Foster, Cherono Kwambai, Parisa A Bahri, Navid R. MoheimaniAbstract:Abstract The successful cultivation of microalgae in wastewater establishes a waste to profit scenario as it combines treatment of a waste stream with production of valuable end-products. Here, growth and nutrient removal efficiency of three different locally isolated microalgal cultures (Chlorella sp., Scenedesmus sp., and a mixed consortium) cultivated in anaerobically digested municipal centrate (ADMC) and anaerobically digested abattoir effluent (ADAE) was evaluated. No significant differences (P > 0.05) in specific growth rate and biomass productivity were recorded between Chlorella monocultures and the mixed culture grown in both effluents. Scenedesmus sp. monocultures was found incapable of growth in both ADMC and ADAE throughout the cultivation period resulting in the collapse of cultures and no further measurements on the growth, biomass production and nutrient removal efficiency of this alga in both effluent. Fq´/Fm´ values which represent the immediate photo-physiological status of microalgae found to be negatively inhibited when Scenedesmus sp. was grown in both effluents throughout the cultivation period. Fq´/Fm´ values of Chlorella sp. monocultures and the mixed cultures recovered back to normal (≈0.6) after an initial drop. Ammonium removal rates was found to be significantly higher (≈2 folds) for Chlorella sp. monocultures grown in both ADMC and ADAE when compared to the mixed cultures. Nonetheless, no significant differences were observed in the removal of phosphate for both cultures in the different effluents. The total protein and carbohydrate content of the biomass produced was similar for both microalgae cultures grown using ADAE and ADMC. However, chlorophyll a and total carotenoids content were found to be higher (P
Jan Wenzel - One of the best experts on this subject based on the ideXlab platform.
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predictive multitask deep neural network models for ADME tox properties learning from large data sets
Journal of Chemical Information and Modeling, 2019Co-Authors: Jan Wenzel, Hans Matter, Friedemann K SchmidtAbstract:Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity) databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyze those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends among multiple ADME-Tox parameters for implicitly correlated data sets. Here we describe a novel, fully industrialized approach to parametrize and optimize the setup, training, application, and visual interpretation of DNNs to model ADME-Tox data. Investigated properties include microsomal lability in different species, passive permeability in Caco-2/TC7 cells, and logD. Statistical models are developed using up to 50 000 compounds from pub...
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Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets
2019Co-Authors: Jan Wenzel, Hans Matter, Friedemann SchmidtAbstract:Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excretion, metabolism, and toxicity) databases are constantly growing, deep neural nets (DNN) emerged as transformative artificial intelligence technology to analyze those challenging data. Relevant features are automatically identified, while appropriate data can also be combined to multitask networks to evaluate hidden trends among multiple ADME-Tox parameters for implicitly correlated data sets. Here we describe a novel, fully industrialized approach to parametrize and optimize the setup, training, application, and visual interpretation of DNNs to model ADME-Tox data. Investigated properties include microsomal lability in different species, passive permeability in Caco-2/TC7 cells, and logD. Statistical models are developed using up to 50 000 compounds from public or corporate databases. Both the choice of DNN hyperparameters and the type and quantity of molecular descriptors were found to be important for successful DNN modeling. Alternate learning of multiple ADME-Tox properties, resulting in a multitask approach, performs statistically superior on most studied data sets in comparison to DNN single-task models and also provides a scalable method to predict ADME-Tox properties from heterogeneous data. For example, predictive quality using external validation sets was improved from R2 of 0.6 to 0.7 comparing single-task and multitask DNN networks from human metabolic lability data. Besides statistical evaluation, a new visualization approach is introduced to interpret DNN models termed “response map”, which is useful to detect local property gradients based on structure fragmentation and derivatization. This method is successfully applied to visualize fragmental contributions to guide further design in drug discovery programs, as illustrated by CRCX3 antagonists and renin inhibitors, respectively
Saeed Alqahtani - One of the best experts on this subject based on the ideXlab platform.
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in silico ADME tox modeling progress and prospects
Expert Opinion on Drug Metabolism & Toxicology, 2017Co-Authors: Saeed AlqahtaniAbstract:ABSTRACTIntroduction: Although significant progress has been made in high-throughput screening of absorption, distribution, metabolism and excretion, and toxicity (ADME-Tox) properties in drug discovery and development, in silico ADME-Tox prediction continues to play an important role in facilitating the appropriate selection of candidate drugs by pharmaceutical companies prior to expensive clinical trials.Areas covered: This review provides an overview of the available in silico models that have been used to predict the ADME-Tox properties of compounds. It also provides a comprehensive overview and summarization of the latest modeling methods and algorithms available for the prediction of physicochemical characteristics, ADME properties, and drug toxicity issues.Expert opinion: The in silico models currently available have greatly contributed to the knowledge of screening approaches in the early stages of drug discovery and the development process. As the definitive goal of in silico molding is to predic...