Insilico Medicine Releases 2025 Annual Results and Advances AI Drug Discovery Platform - MLQ.ai
Hey there, little scientist! 🧪
Imagine a super-duper smart robot friend named MLQ.ai! This robot lives at a company called Insilico Medicine.
MLQ.ai is like a detective, but instead of finding clues for a mystery, it helps find new medicines! 💊 It's super fast at looking at lots and lots of tiny pieces, like LEGOs, to figure out how to build new medicines to make people feel better when they're sick.
The company just told everyone that their robot friend MLQ.ai is getting even smarter and better at its job! Yay for smart robots helping us! 🎉
<a href="https://news.google.com/rss/articles/CBMiqwFBVV95cUxOa2ktRW5nODFmV1ZkMXNoMTRpTnlYWFQyb29aR3hwWmoxN081VTNMM3p2UFBPT2o2cjVUUkdxdk11ajlJT1VjUHBRR1RZWHJQZEdYdUVFNGx2VV84Uk11RjVaRzBrejZHQnJIT1JXdnFBVkl0UktiN2o3OGpraW41RmpsUXN3VG5mMXcybWhRMEJaMm1ZSUIwWEx1akxvOFJDNG1Oc29Vbmw0dEk?oc=5" target="_blank">Insilico Medicine Releases 2025 Annual Results and Advances AI Drug Discovery Platform</a> <font color="#6f6f6f">MLQ.ai</font>
Could not retrieve the full article text.
Read on GNews AI drug discovery →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
releaseplatform![[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-microchip-RD7Ub6Tkp8JwbZxSThJdV5.webp)
[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?
In the past year you may have encountered the following prompt: The surgeon, who is the boy's father, says, 'I cannot operate on this boy—he's my son!'. Who is the surgeon to the boy? If you try to give this prompt to an LLM right now you will probably still receive “The mother” as an answer, even though the text explicitly states that the surgeon is the boy’s father; this is probably due to the fact that this prompt is an alteration of a very common “riddle”, to which the answer is, in fact, the mother: A man and his son are in a terrible accident and are rushed to the hospital in critical condition. The doctor looks at the boy and exclaims, "I can't operate on this boy; he's my son!" How could this be? Working on this failure mode, I initially decided to create a small dataset of altered
A Multi-Language Perspective on the Robustness of LLM Code Generation
arXiv:2504.19108v5 Announce Type: replace Abstract: Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performanc
Separating Oblivious and Adaptive Differential Privacy under Continual Observation
arXiv:2603.11029v2 Announce Type: replace-cross Abstract: We resolve an open question of Jain, Raskhodnikova, Sivakumar, and Smith (ICML 2023) by exhibiting a problem separating differential privacy under continual observation in the oblivious and adaptive settings. The continual observation (a.k.a. continual release) model formalizes privacy for streaming algorithms, where data is received over time and output is released at each time step. In the oblivious setting, privacy need only hold for data streams fixed in advance; in the adaptive setting, privacy is required even for streams that can be chosen adaptively based on the streaming algorithm's output. We describe the first explicit separation between the oblivious and adaptive settings. The problem showing this separation is based on
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
Hong Kong developers test homebuyers with modest price increases after sell-outs
Hong Kong developers are raising prices of new homes this week following sold-out launches in recent days, further testing the appetite of homebuyers amid geopolitical and interest rate uncertainties. Henderson Land Development put another 39 units at its Chester project in Hung Hom on sale on Monday, with 25 homes finding buyers, according to agents. With an average discounted price of HK$22,198 (US$2,831) per square foot, the units were priced 4.57 per cent higher than the 123 units that sold...
Why Microservices Struggle With AI Systems
Adding AI to microservices breaks the assumption that same input produces same output, causing unpredictability, debugging headaches, and unreliable systems. To safely integrate AI, validate outputs, version prompts, use a control layer, and implement rule-based fallbacks. Never let AI decide alone—treat it as advisory, not authoritative. Read All
An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
arXiv:2509.19185v3 Announce Type: replace Abstract: Foundation model (FM)-based AI agents are rapidly gaining adoption across diverse domains, but their inherent non-determinism and non-reproducibility pose testing and quality assurance challenges. While recent benchmarks provide task-level evaluations, there is limited understanding of how developers verify the internal correctness of these agents during development. To address this gap, we conduct the first large-scale empirical study of testing practices in the AI agent ecosystem, analyzing 39 open-source agent frameworks and 439 agentic applications. We identify ten distinct testing patterns and find that novel, agent-specific methods like DeepEval are seldom used (around 1%), while traditional patterns like negative and membership tes
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!