Abstract: In an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. Playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. A semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. These semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. Based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.

Abstract: In an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. Playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. A semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. These semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. Based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.

Abstract: In an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. Playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. A semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. These semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. Based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.

Abstract: New types of hydroxamic acid-based bifunctional chelators are provided. These chelators are designed to chelate metal ions that can be detected either by their paramagnetic or radioactive properties. Conjugation with peptides or protein can be achieved by the presence of a linker moiety in the molecular structure of these chelators.

Abstract: New types of hydroxamic acid-based bifunctional chelators are provided. These chelators are designed to chelate metal ions that can be detected either by their paramagnetic or radioactive properties. Conjugation with peptides or protein can be achieved by the presence of a linker moiety in the molecular structure of these chelators.

Abstract: New types of hydroxamic acid-based bifunctional chelators are provided. These chelators are designed to chelate metal ions that can be detected either by their paramagnetic or radioactive properties. Conjugation with peptides or protein can be achieved by the presence of a linker moiety in the molecular structure of these chelators.

Abstract: New types of hydroxamic acid-based bifunctional chelators are provided. These chelators are designed to chelate metal ions that can be detected either by their paramagnetic or radioactive properties. Conjugation with peptides or protein can be achieved by the presence of a linker moiety in the molecular structure of these chelators.

Abstract: Novel N.sub.3 S.sub.2 chelating ligands can be chelated to a radiodetectable element, Tc or Re, and are useful for radiolabeling peptides. A group of peptides especially useful are somatostatin derivatives which are valuable of the selective detection or treatment of tumors with somatostatin receptors.

Abstract: Technetium or rhenium radionuclide labelled chelate-hexapeptide complexes are useful for the selective detection or treatment of tumors with somatostatin receptors. In particular, compounds of formula: ##STR1## (wherein R, R.sup.1, R.sup.3 and A are as defined herein) incorporating an N3S2 chelate may be labelled with a radionuclide. The resulting complexes are useful as radioimaging agents or as radiopharmaceuticals for the treatment of certain tumors.

Abstract: The ligands HBED-SCN, HBPD-SCN, and HTDD-SCN are provided which have enhanced ease of reaction with peptides or proteins and which are suitable for chelating with radioisoptopes, especially Indium-.sup.III and Gallium-.sup.67.