Suggestions
Xing Che
CEO at YDS Pharmatech
Xing Che is an Editorial Manager at Elsevier who recently joined the editor team at Artificial Intelligence Chemistry, a journal published by Elsevier.1 As an Editorial Manager, Che likely oversees the editorial process and manages submissions for scientific journals in the field of chemistry, particularly those related to artificial intelligence applications in chemistry. Their LinkedIn username is xing-che, as mentioned in the query.
Che appears to have expertise in the intersection of artificial intelligence and chemistry, given their involvement with the Artificial Intelligence Chemistry journal. They expressed enthusiasm about joining this editorial team, noting the "active contributions from AI drug discovery and material science" in the field.1 This suggests Che has knowledge of how AI is being applied to areas like drug discovery and materials science within chemistry research.
While specific details about Che's background or other roles are not provided in the limited information available, their position as an Editorial Manager at a major scientific publisher like Elsevier indicates they likely have significant experience in academic publishing and scientific editing, particularly in chemistry-related fields.
Highlights
Reading financial reports often involves hours of meticulous analysis, connecting data dots, and extracting insights. With Anthropic Claude 3.5 Sonnet, this process becomes much more manageable and insightful. It's incredibly reader-friendly and allows us to generate demo dashboard in just a few minutes.
Prompt: Create an interactive Business intelligence infographics report of this Schrödinger Q1 2024 Update with financial and drug discovery highlights
#FinancialAnalysis #AI #Anthropic #ProductivityHack
A colab for protein structure tokenization: ProToken--https://lnkd.in/gu6y9RQ9. Some another works on this topic are e.g. Foldseek and SaProt. What is protein structure tokenization? You can view it as a structure-aware vocabulary. Why it's important? It enables LLM understands protein structure, then leveraging power of LLM, we can conduct various downstream tasks like protein-protein interaction, protein structural prediction, fitness landscape prediction and protein design. It will be paradigm shifting. This will bring up a new avenue compared to physics-based approaches and current generative AI approaches like using diffusion model for protein design. We can also leverage all LLM techs like RAG, MoE to power up the model etc. Lots of potential down this path. #ProteinStructureTokenization #LLM #ProteinDesign #MachineLearning #AI #PPI
