Comparison of scaling methods to obtain calibrated probabilities of activity for protein–ligand predictions
In the context of bioactivity prediction, the question of how to calibrate a score produced by a
machine learning method into a probability of binding to a protein target is not yet …
machine learning method into a probability of binding to a protein target is not yet …
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
Measurements of protein–ligand interactions have reproducibility limits due to experimental
errors. Any model based on such assays will consequentially have such unavoidable errors …
errors. Any model based on such assays will consequentially have such unavoidable errors …
Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins
N Sugaya - Journal of chemical information and modeling, 2014 - ACS Publications
The concept of ligand efficiency (LE) indices is widely accepted throughout the drug design
community and is frequently used in a retrospective manner in the process of drug …
community and is frequently used in a retrospective manner in the process of drug …
Comparing global and local likelihood score thresholds in multiclass laplacian-modified naive bayes protein target prediction
G Drakakis, A Koutsoukas… - … Chemistry & High …, 2015 - ingentaconnect.com
The increase of publicly available bioactivity data has led to the extensive development and
usage of in silico bioactivity prediction algorithms. A particularly popular approach for such …
usage of in silico bioactivity prediction algorithms. A particularly popular approach for such …
Dynamic applicability domain (dAD): compound–target binding affinity estimates with local conformal prediction
Motivation Increasing efforts are being made in the field of machine learning to advance the
learning of robust and accurate models from experimentally measured data and enable …
learning of robust and accurate models from experimentally measured data and enable …
Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics
We compared two algorithms for ligand-target prediction, namely, the Laplacian-modified
Bayesian classifier and the Winnow algorithm. A dataset derived from the WOMBAT …
Bayesian classifier and the Winnow algorithm. A dataset derived from the WOMBAT …
An analysis of proteochemometric and conformal prediction machine learning protein-ligand binding affinity models
Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole
reliance on experimental design, make, and test cycles is costly and time consuming …
reliance on experimental design, make, and test cycles is costly and time consuming …
Validation strategies for target prediction methods
Computational methods for target prediction, based on molecular similarity and network-
based approaches, machine learning, docking and others, have evolved as valuable and …
based approaches, machine learning, docking and others, have evolved as valuable and …
Latent biases in machine learning models for predicting binding affinities using popular data sets
Drug design involves the process of identifying and designing molecules that bind well to a
given receptor. A vital computational component of this process is the protein–ligand …
given receptor. A vital computational component of this process is the protein–ligand …
Predicting the reliability of drug-target interaction predictions with maximum coverage of target space
Many computational methods to predict the macromolecular targets of small organic
molecules have been presented to date. Despite progress, target prediction methods still …
molecules have been presented to date. Despite progress, target prediction methods still …