The past few years have witnessed enormous progress toward
applying
machine learning approaches to the development of protein–ligand
scoring functions. However, the robust performance and wide applicability
of scoring functions remain a big challenge for increasing the success
rate of docking-based virtual screening. Herein, a novel scoring function
named RTMScore was developed by introducing a tailored residue-based
graph representation strategy and several graph transformer layers
for the learning of protein and ligand representations, followed by
a mixture density network to obtain residue–atom distance likelihood
potential. Our approach was resolutely validated on the CASF-2016
benchmark, and the results indicate that RTMScore can outperform almost
all of the other state-of-the-art methods in terms of both the docking
and screening powers. Further evaluation confirms the robustness of
our approach that can not only retain its docking power on cross-docked
poses but also achieve improved performance as a rescoring tool in
larger-scale virtual screening.
Proteolysis targeting chimeras (PROTACs), which harness the ubiquitin-proteasome system to selectively induce targeted protein degradation, represent an emerging therapeutic technology with the potential to modulate traditional undruggable targets. Over the past few years, this technology has moved from academia to industry and more than 10 PROTACs have been advanced into clinical trials. However, designing potent PROTACs with desirable drug-like properties still remains a great challenge. Here, we report an updated online database, PROTAC-DB 2.0, which is a repository of structural and experimental data about PROTACs. In this 2nd release, we expanded the number of PROTACs to 3270, which corresponds to a 96% expansion over the first version. Meanwhile, the numbers of warheads (small molecules targeting the proteins of interest), linkers, and E3 ligands (small molecules recruiting E3 ligases) have increased to over 360, 1500 and 80, respectively. In addition, given the importance and the limited number of the crystal target-PROTAC-E3 ternary complex structures, we provide the predicted ternary complex structures for PROTACs with good degradation capability using our PROTAC-Model method. To further facilitate the analysis of PROTAC data, a new filtering strategy based on the E3 ligases is also added. PROTAC-DB 2.0 is available online at http://cadd.zju.edu.cn/protacdb/.
Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring...
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