Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?
Binding affinity prediction largely determines the discovery efficiency of lead compounds in
drug discovery. Recently, machine learning (ML)-based approaches have attracted much …
drug discovery. Recently, machine learning (ML)-based approaches have attracted much …
Visualizing convolutional neural network protein-ligand scoring
Protein-ligand scoring is an important step in a structure-based drug design pipeline.
Selecting a correct binding pose and predicting the binding affinity of a protein-ligand …
Selecting a correct binding pose and predicting the binding affinity of a protein-ligand …
A hybrid structure-based machine learning approach for predicting kinase inhibition by small molecules
C Liu, P Kutchukian, ND Nguyen… - Journal of Chemical …, 2023 - ACS Publications
Kinases have been the focus of drug discovery programs for three decades leading to over
70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 …
70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 …
Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions
How to accurately estimate protein–ligand binding affinity remains a key challenge in
computer-aided drug design (CADD). In many cases, it has been shown that the binding …
computer-aided drug design (CADD). In many cases, it has been shown that the binding …
Assessment of the generalization abilities of machine-learning scoring functions for structure-based virtual screening
In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein–
ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a …
ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a …
TB-IECS: an accurate machine learning-based scoring function for virtual screening
Abstract Machine learning-based scoring functions (MLSFs) have shown potential for
improving virtual screening capabilities over classical scoring functions (SFs). Due to the …
improving virtual screening capabilities over classical scoring functions (SFs). Due to the …
Tapping on the black box: how is the scoring power of a machine-learning scoring function dependent on the training set?
In recent years, protein–ligand interaction scoring functions derived through machine-
learning are repeatedly reported to outperform conventional scoring functions. However …
learning are repeatedly reported to outperform conventional scoring functions. However …
[HTML][HTML] Open source molecular modeling
The success of molecular modeling and computational chemistry efforts are, by definition,
dependent on quality software applications. Open source software development provides …
dependent on quality software applications. Open source software development provides …
Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry
SA Kumar, TD Ananda Kumar… - Future Medicinal …, 2022 - Taylor & Francis
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …
optimization in drug discovery research, requires molecular representation. Previous reports …
Beware of simple methods for structure-based virtual screening: the critical importance of broader comparisons
VK Tran-Nguyen, PJ Ballester - Journal of Chemical Information …, 2023 - ACS Publications
We discuss how data unbiasing and simple methods such as protein-ligand Interaction
FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is …
FingerPrint (IFP) can overestimate virtual screening performance. We also show that IFP is …