Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model

Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical reactions. In this paper, we have proposed a unified framework that addresses both the reaction representation learning and molecule generation tasks, which allows for a more holistic approach. Inspired by the organic chemistry mechanism, we develop a novel pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results on challenging downstream tasks. By possessing chemical knowledge, our generative framework overcome the limitations of current molecule generation models that rely on a small number of reaction templates. In the extensive experiments, our model generates synthesizable drug-like structures of high quality. Overall, our work presents a significant step toward a large-scale deep-learning framework for a variety of reaction-based applications

Proteostasis by STUB1/HSP70 complex controls sensitivity to androgen receptor targeted therapy in advanced prostate cancer.

Protein homeostasis (proteostasis) is a potential mechanism that contributes to cancer cell survival and drug resistance. Constitutively active androgen receptor (AR) variants confer anti-androgen resistance in advanced prostate cancer. However, the role of proteostasis involved in next generation anti-androgen resistance and the mechanisms of AR variant regulation are poorly defined. Here we show that the ubiquitin-proteasome-system (UPS) is suppressed in enzalutamide/abiraterone resistant prostate cancer. AR/AR-V7 proteostasis requires the interaction of E3 ubiquitin ligase STUB1 and HSP70 complex. STUB1 disassociates AR/AR-V7 from HSP70, leading to AR/AR-V7 ubiquitination and degradation. Inhibition of HSP70 significantly inhibits prostate tumor growth and improves enzalutamide/abiraterone treatments through AR/AR-V7 suppression. Clinically, HSP70 expression is upregulated and correlated with AR/AR-V7 levels in high Gleason score prostate tumors. Our results reveal a novel mechanism of anti-androgen resistance via UPS alteration which could be targeted through inhibition of HSP70 to reduce AR-V7 expression and overcome resistance to AR-targeted therapies

Identification of the ADPR binding pocket in the NUDT9 homology domain of TRPM2

Activation of the transient receptor potential melastatin 2 (TRPM2) channel occurs during the response to oxidative stress under physiological conditions as well as in pathological processes such as ischemia and diabetes. Accumulating evidence indicates that adenosine diphosphate ribose (ADPR) is the most important endogenous ligand of TRPM2. However, although it is known that ADPR binds to the NUDT9 homology (NUDT9-H) domain in the intracellular C-terminal region, the molecular mechanism underlying ADPR binding and activation of TRPM2 remains unknown. In this study, we generate a structural model of the NUDT9-H domain and identify the binding pocket for ADPR using induced docking and molecular dynamics simulation. We find a subset of 11 residues—H1346, T1347, T1349, L1379, G1389, S1391, E1409, D1431, R1433, L1484, and H1488—that are most likely to directly interact with ADPR. Results from mutagenesis and electrophysiology approaches support the predicted binding mechanism, indicating that ADPR binds tightly to the NUDT9-H domain, and suggest that the most significant interactions are the van der Waals forces with S1391 and L1484, polar solvation interaction with E1409, and electronic interactions (including π–π interactions) with H1346, T1347, Y1349, D1431, and H1488. These findings not only clarify the roles of a range of newly identified residues involved in ADPR binding in the TRPM2 channel, but also reveal the binding pocket for ADPR in the NUDT9-H domain, which should facilitate structure-based drug design for the TRPM2 channel

Surgical management of adrenal cysts: a single-institution experience

Objective To analyze surgical methods and evaluate treatment efficacy and safety for managing adrenal cystic lesions. Materials and methods All patients presenting with adrenal lesions of the West China Hospital were reviewed retrospectively from January 2003 to April 2013 and 47 were diagnosed as adrenal cysts. Basic information, clinical history, physical examination, laboratory investigations, abdominal ultrasound and enhanced computed tomography were detailed noted. Cysts with different surgical management were analyzed and surgery option operative time, postoperative complications and after-surgery hospital stay were all noted. The final diagnosis was judged by histopathology. Patients were followed from 3 month to 10 years. Results All the 47 patients with a mean age of 43.8 years were managed by surgical intervention. Compared laparoscopic technology with open technology, the laparoscopic has the advantage of a shorter operation time, shorter hospital stay after surgery and enhanced cosmesis. The histopathologic result was: 23 (50%) were endothelial cysts and 16 (35%) were pseudocysts. One patient had evidence to recurrence at the followed-up stage. Conclusion Adrenal cysts are rare and with the development of imaging techniques many of these are diagnosed incidentally. CT has advantages in detecting the cysts with haemorrhage, intracystic debris, calcification and mixed adrenal mass. Minimally invasive surgery offers equivalent efficacy to traditional open procedures, while providing a shorter operation time, shorter convalescence and improved cosmesis. Patients after surgical resection should be followed up closely especially if functional cysts and histopathology of cystic tumor are present

MOESM1 of Multi-objective de novo drug design with conditional graph generative model

Additional file 1. Containing additional information about the implementation details of experiments

MOESM2 of Multi-objective de novo drug design with conditional graph generative model

Additional file 2. Contianing supplementary figures