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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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. by Alexander Kroll (9304225)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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. by Alexander Kroll (9304225)

Subjects:

ProSmith model overview. by Alexander Kroll (9304225)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

Python software for calculating the area under a curve by 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 by Débora Elisa da Costa Matoso (5255054)

Resolving Harvesting Errors in Institutional Repository Migration : Using Python Scripts with VS Code and LLM Integration. by 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 by 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. …”