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Supplement WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking T able of Contents

Neural Information Processing Systems

If taking a closer look at the MedDRA classification on the system organ level on its website, we can find a claim of "System Organ Classes (SOCs) which are groupings by aetiology (e.g. However, as claimed in the original paper, "It should be noted that we did not perform any preprocessing of our datasets, such as Tab. These datasets appear in MoleculeNet as well. As mentioned in the introduction in the main paper, there are also issues with inconsistent representations and undefined stereochemistry. We list an example for each in Figure 1 and Figure 1.






'Trump will be gone in three years': Top US Democrats try to reassure Europe

BBC News

'Trump will be gone in three years': Top Democrats try to reassure Europe US Secretary of State Marco Rubio was the centre of attention at the Munich Security Summit, as European leaders wondered apprehensively what tone he would strike in his remarks on Saturday. While his speech did not fully allay their concerns, it has been viewed as a reassurance to allies that while US relations may have frayed under Donald Trump, they will not break. Rubio's was not the only American political voice at the security summit, however. And even if the secretary of state's remarks had not been so well-received - if he had sharply criticised Europeans the way Vice-President JD Vance did at the conference last year - there were other American politicians doing their best impression of the Persian poet, counselling: This too shall pass. If there's nothing else I can communicate today, California Governor Gavin Newsom said at a conference event on Friday, Donald Trump is temporary.


Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions

Neural Information Processing Systems

Contributions: In this work, we develop a projected Stein variational Newton method (pSVN) to tackle the challenge of high-dimensional Bayesian inference by exploiting the intrinsic lowdimensional geometric structure of the posterior distribution (where it departs from the prior), as characterized by the dominant spectrum of the prior-preconditioned Hessian of the negative log likelihood.