Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate thi...
Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly...
Journal of biomolecular structure & dynamics
Jan 10, 2022
Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly custo...
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...
Journal of chemical information and modeling
Jan 3, 2022
Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the ...
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...
Journal of chemical information and modeling
Dec 10, 2021
We present results on the extent to which physics-based simulation (exemplified by FEP) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibit...
Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-di...
In this paper, we propose a simple descriptor called the ligand coordinate profile (LCP) for describing docking poses. The LCP descriptor is generated from the coordinates of the polar hydrogen and heavy atoms of the docked ligand. We hypothesize tha...
International journal of molecular sciences
Nov 28, 2021
In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection a...