PolyCL: contrastive learning for polymer representation learning explicit and implicit augmentations.

Polymers play a crucial role in a wide array of applications due to their diverse and tunable properties. Establishing the relationship between polymer representations and their properties is crucial to the computational design and screening of potential polymers machine learning. The quality of the representation significantly influences the effectiveness of these computational methods.

Adverse Events of Radioligand Therapy in Patients with Progressive Neuroendocrine Neoplasms: The Biggest Eastern European Prospective Study.

Background: Neuroendocrine neoplasms (NENs) are neoplastic tumors developing in every part of the body, mainly in the gastrointestinal tract and pancreas. Their treatment involves the surgical removal of the tumor and its metastasis, long-acting somatostatin analogs, chemotherapy, targeted therapy, and radioligand therapy (RLT).

Materials And Methods: A total of 127 patients with progressive neuroendocrine neoplasms underwent RLT-4 courses, administered every 10 weeks-with the use of 7.

Web-BO: towards increased accessibility of Bayesian optimisation (BO) for chemistry.

Historically, the chemical discovery process has predominantly been a matter of trial-and-improvement, where small modifications are made to a chemical system, guided by chemical knowledge, with the aim of optimising towards a target property or combination of properties. While a trial-and-improvement approach is frequently successful, especially when assisted by the help of serendipity, the approach is incredibly time- and resource-intensive. Complicating this further, the available chemical space that could, in theory, be explored is remarkably vast.