Reception Test of Petals for the End Cap TEC+ of the CMS Silicon Strip Tracker

The silicon strip tracker of the CMS experiment has been completed and was inserted into the CMS detector in late 2007. The largest sub system of the tracker are its end caps, comprising two large end caps (TEC) each containing 3200 silicon strip modules. To ease construction, the end caps feature a modular design: groups of about 20 silicon modules are placed on sub-assemblies called petals and these self-contained elements are then mounted onto the TEC support structures. Each end cap consists of 144 such petals, which were built and fully qualified by several institutes across Europe. Fro

Integration of the End Cap TEC+ of the CMS Silicon Strip Tracker

The silicon strip tracker of the CMS experiment has been completed and inserted into the CMS detector in late 2007. The largest sub-system of the tracker is its end cap system, comprising two large end caps (TEC) each containing 3200 silicon strip modules. To ease construction, the end caps feature a modular design: groups of about 20 silicon modules are placed on sub-assemblies called petals and these self-contained elements are then mounted into the TEC support structures. Each end cap consists of 144 petals, and the insertion of these petals into the end cap structure is referred to as TEC integration. The two end caps were integrated independently in Aachen (TEC+) and at CERN (TEC--). This note deals with the integration of TEC+, describing procedures for end cap integration and for quality control during testing of integrated sections of the end cap and presenting results from the testing

Deep interactome learning for de novo drug design

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It allows for the construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma were generated. The top-ranking designs were chemically synthesized and biophysically and biochemically characterized. Potent PPAR partial agonists were identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirmed the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules

Petal Integration for the CMS Tracker End Caps

This note describes the assembly and testing of the 292 petals built for the CMS Tracker End Caps from the beginning of 2005 until the summer of 2006. Due to the large number of petals to be assembled and the need to reach a throughput of 10 to 15 petals per week, a distributed integration approach was chosen. This integration was carried out by the following institutes: I. and III. Physikalisches Institut - RWTH Aachen University; IIHE, ULB \& VUB Universities, Brussels; Hamburg University; IEKP, Karlsruhe University; FYNU, Louvain University; IPN, Lyon University; and IPHC, Strasbourg University. Despite the large number of petals which needed to be reworked to cope with a late-discovered module issue, the quality of the petals is excellent with less than 0.2\% bad channels