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.

The bitter feud between Elon Musk and OpenAI boss Sam Altman has raged for years, but has mostly played out online in the form of accusations, counter-accusations and jibes. But starting on Tuesday, the beef between the two tech billionaires will shift to a much higher-profile forum: a federal courtroom in California, where their row will be the focus of a month-long trial. Being considered is Musk's claim that Altman - with whom he founded OpenAI - has swindled him out of millions of dollars and reneged on the ChatGPT-maker's original non-profit mission. Musk and Altman themselves will be among those to testify in a case in which the future of AI could be at stake. And while one will presumably emerge the winner, it's plausible that neither will emerge from the saga unscathed.

Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we focus on the complex yet highly realistic task of incomplete multi-view weak multi-label learning and propose a masked two-channel decoupling framework based on deep neural networks to solve this problem. The core innovation of our method lies in decoupling the singlechannel view-level representation, which is common in deep multi-view learning methods, into a shared representation and a view-proprietary representation. We also design a cross-channel contrastive loss to enhance the semantic property of the two channels. Additionally, we exploit supervised information to design a labelguided graph regularization loss, helping the extracted embedding features preserve the geometric structure among samples. Inspired by the success of masking mechanisms in image and text analysis, we develop a random fragment masking strategy for vector features to improve the learning ability of encoders. Finally, it is important to emphasize that our model is fully adaptable to arbitrary view and label absences while also performing well on the ideal full data. We have conducted sufficient and convincing experiments to confirm the effectiveness and advancement of our model.