The optimal ProSmith models combine predictions based on the multimodal Transformer Network with predictions based on separate numerical representations of proteins and small molec... حسب Alexander Kroll (9304225)

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Performance metrics for ProSmith and previously published methods for DTA prediction for different splitting scenarios. حسب Alexander Kroll (9304225)

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For accurate DTA predictions, ProSmith requires training on identical drugs but not on similar proteins. حسب Alexander Kroll (9304225)

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Replacing the final prediction layer of end-to-end trained Transformer Networks with gradient boosting models improves performance. حسب Alexander Kroll (9304225)

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Coefficient of determination <i>R</i>² of ProSmith for different splitting scenarios of the Davis dataset. حسب Alexander Kroll (9304225)

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We divided the ESP test set into subsets according to the number of positive data points with the same small molecule in the training set. حسب Alexander Kroll (9304225)

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Performance metrics for ProSmith and ESP for the prediction of enzyme-substrate pairs. حسب Alexander Kroll (9304225)

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We divided the ESP test set into subsets according to the maximal sequence identity of an enzyme compared to all training enzymes. حسب Alexander Kroll (9304225)

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Drug-target affinity predictions improve with increasing drug similarity score compared to the training drugs. حسب Alexander Kroll (9304225)

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Performance metrics of ProSmith and previously published methods for the prediction of Michaelis constants <i>K</i>_M. حسب Alexander Kroll (9304225)

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ProSmith outperforms previous models in the prediction of Michaelis constants <i>K</i>_M especially for enzymes not highly similar to proteins in the training set. حسب Alexander Kroll (9304225)

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ProSmith model overview. حسب Alexander Kroll (9304225)

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Hyperparameters of the ProSmith Transformer Network. حسب Alexander Kroll (9304225)

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Enzyme-substrate pair predictions improve with increasing substrate similarity compared to training substrates. حسب Alexander Kroll (9304225)

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Hyperparameters of the ProSmith gradient boosting models. حسب Alexander Kroll (9304225)

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Optimal implementation of matrix operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY2D;... حسب Xuzhen He (14676114)

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Python software for calculating the area under a curve حسب Wenfa Ng (444092)

"…Modularity in the software code ensures that the Python software can be readily adapted to calculate the area under the curve for other mathematical functions. …"

Multiparametric tuning and classification of motor neuron model to represent ALS conditions حسب Débora Elisa da Costa Matoso (5255054)

Resolving Harvesting Errors in Institutional Repository Migration : Using Python Scripts with VS Code and LLM Integration. حسب satoshi hashimoto(橋本 郷史) (18851272)

"…Therefore, we decided to create a dedicated Python program using Large Language Model (LLM)-assisted coding.…"

Collision Efficiency Parameter Influence on Pressure-Dependent Rate Constant Calculations Using the SS-QRRK Theory حسب E. Grajales-González (6083633)

"…To overcome this underestimation problem, we evaluated and implemented in a bespoke Python code two alternative definitions for the collision efficiency using the SS-QRRK theory and tested their performance by comparing the pressure-dependent rate constants with the Rice–Ramsperger–Kassel–Marcus/Master Equation (RRKM/ME) results. …"