May 24 Issue

May issue now live

Fürrutter, F., Muñoz-Gil, G. & Briegel, H.J. Quantum circuit synthesis with diffusion models.. 

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  • Predicting the binding affinity between small-molecule ligands and proteins is a key task in drug discovery; however, sequence-based methods are often less accurate than structure-based ones. Koh et al. develop a graph neural network using physicochemical constraints that discovers interactions between small molecules and proteins directly from sequence data and that can achieve state-of-the-art performance without the need for costly, experimental 3D structures.

    • Huan Yee Koh
    • Anh T. N. Nguyen
    • Geoffrey I. Webb
  • Deep reinforcement learning (RL) has been successful in many fields but has not been used to directly improve behaviours by interfacing with living nervous systems. Li et al. present a framework that integrates deep RL agents with the nervous system of the nematode Caenorhabditis elegans. Their study shows that trained agents can assist animals in biologically relevant tasks and can be studied after training to map out effective neural policies.

    • Chenguang Li
    • Gabriel Kreiman
    • Sharad Ramanathan
  • Machine learning can improve scoring methods to evaluate protein–ligand interactions, but achieving good generalization is an outstanding challenge. Cao et al. introduce EquiScore, which is based on a graph neural network that integrates physical knowledge and is shown to have robust capabilities when applied to unseen protein targets.

    • Duanhua Cao
    • Geng Chen
    • Mingyue Zheng
  • The credit assignment problem involves assigning credit to synapses in a neural network so that weights are updated appropriately and the circuit learns. Max et al. developed an efficient solution to the weight transport problem in networks of biophysical neurons. The method exploits noise as an information carrier and enables networks to learn to solve a task efficiently.

    • Kevin Max
    • Laura Kriener
    • Mihai A. Petrovici
  • Bolstering the broad and deep applicability of graph neural networks, Heydaribeni et al. introduce HypOp, a framework that uses hypergraph neural networks to solve general constrained combinatorial optimization problems. The presented method scales and generalizes well, improves accuracy and outperforms existing solvers on various benchmarking examples.

    • Nasimeh Heydaribeni
    • Xinrui Zhan
    • Farinaz Koushanfar
  • Deep learning has led to great advances in predicting protein structure from sequences. Ren and colleagues present here a method for the inverse problem of finding a sequence that results in a desired protein structure, which is inspired by various components of AlphaFold combined with Markov random fields to decode sequences more efficiently.

    • Milong Ren
    • Chungong Yu
    • Haicang Zhang
  • Personalized LLMs built with the capacity for emulating empathy are right around the corner. The effects on individual users need careful consideration.

  • Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning methods, with more focus on the development of a basic understanding grounded in statistical theory.

    • Diego Marcondes
    • Adilson Simonis
    • Junior Barrera
  • Research papers can make a long-lasting impact when the code and software tools supporting the findings are made readily available and can be reused and built on. Our reusability reports explore and highlight examples of good code sharing practices.

  • Speech technology offers many applications to enhance employee productivity and efficiency. Yet new dangers arise for marginalized groups, potentially jeopardizing organizational efforts to promote workplace diversity. Our analysis delves into three critical risks of speech technology and offers guidance for mitigating these risks responsibly.

    • Mike Horia Mihail Teodorescu
    • Mingang K. Geiger
    • Lily Morse