In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atomlevel-based methods, which consider atoms as basic components and generate atom positions and types. In this way, however, it is hard to generate realistic fragments with complicated structures. To solve this, we propose D3FG, a functional-groupbased diffusion model for pocket-specific molecule generation and elaboration. D3FG decomposes molecules into two categories of components: functional groups defined as rigid bodies and linkers as mass points. And the two kinds of components can together form complicated fragments that enhance ligand-protein interactions. To be specific, in the diffusion process, D3FG diffuses the data distribution of the positions, orientations, and types of the components into a prior distribution; In the generative process, the noise is gradually removed from the three variables by denoisers parameterized with designed equivariant graph neural networks. In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties. Besides, D3FG as a solution to a new task of molecule elaboration, could generate molecules with high affinities based on existing ligands and the hotspots of target proteins.

Plus: OpenAI has ended its exclusive partnership with Microsoft. Elon Musk and Sam Altman are going to court over OpenAI's future Ahead of OpenAI's IPO, the court could rule on whether the company can exist as a for-profit enterprise. It could even oust its leadership. Musk, an OpenAI co-founder, claims he was deceived into bankrolling the firm under false pretenses. Find out how the trial could upend the global AI race . In a celebrated episode, a community of gnomes sneak out at night to steal underpants.

Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and theoretical limitations in inference and model learning. We introduce SIXO, a method that instead learns target distributions that approximate the smoothing distributions, incorporating information from all observations. The key idea is to use density ratio estimation to fit functions that warp the filtering distributions into the smoothing distributions. We then use SMC with these learned targets to define a variational objective for model and proposal learning. SIXO yields provably tighter log marginal lower bounds and offers more accurate posterior inferences and parameter estimates in a variety of domains.

The answer may already be in your home. To achieve a smart home, you need a voice assistant to run it. A smart home assistant, usually folded into a smart speaker, will let you command your smart home with your voice and run your various routines. It also acts as a center for every gadget you want to add to your home. And you can add almost anything these days, from smart garage control to even voice-commanding your blinds .

Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery. Previous work focused on supervised learning methods for small molecules where binding affinity data is abundant, but it is hard to apply the same strategy to other ligand classes like antibodies where labelled data is limited. In this paper, we explore unsupervised approaches and reformulate binding energy prediction as a generative modeling task. Specifically, we train an energy-based model on a set of unlabelled protein-ligand complexes using SE(3) denoising score matching (DSM) and interpret its log-likelihood as binding affinity. Our key contribution is a new equivariant rotation prediction network for SE(3) DSM called Neural Euler's Rotation Equations (NERE). It predicts a rotation by modeling the force and torque between protein and ligand atoms, where the force is defined as the gradient of an energy function with respect to atom coordinates. Using two protein-ligand and antibody-antigen binding affinity prediction benchmarks, we show that NERE outperforms all unsupervised baselines (physics-based potentials and protein language models) in both cases and surpasses supervised baselines in the antibody case.

The model can easily distinguish A and B according to the background (i.e., the so-called geometric skews [26]), but not according to the features of the class instance itself. However, if there is another class C, which is also in black background. In this tri-classification task (distinguishing A,B, and C), an ideal model should focus on the feature of the instance itself but not the background. This is one of the difficulties: distribution bias on samples, that some beneficial features (e.g., background) may be good for the classification, but not good for understanding the class (in a compositional way). Another difficulty is entanglement of the labels. We provide the labels in a relative way that the label of A is '0' and of B is '1', but not their true textual meanings (e.g., white paper and green leaves). The concept information is entangled and embedded into the label, thus, it is hard for the model to tell which visual features capture the corresponding concepts (i.e., white refers to the color feature and paper refers to the texture feature). We hope our understanding of this issue can inspire researchers to focus more on compositionality and design excellent continual learners.

Wizards of the Coast are seeking'a say in layoffs, accountability and a living wage.' The CWA says it has secured a supermajority among workers in favor of unionization for the chapter, called United Wizards of the Coast (UWOTC-CWA). The CWA has filed for a formal election with the National Labor Relations Board (NLRB), but that will be withdrawn if Hasbro voluntarily recognizes the union by May 1st. At Wizards, we're organizing for a say in layoffs, accountability that runs up and down the chain, and a living wage that actually lets people build a life, said UWOTC-CWA member and senior software engineer Damien Wilson. I'm hopeful about what we can build here and being clear-eyed about why it's necessary. Workers have outlined several areas of concern including protections over layoffs and remote work, generative AI guardrails and mandatory crunch time, along with increased transparency and equity in the workplace.