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Generative models for chemical structures are often trained to create output in the common SMILES notation. Michael Skinnider shows that training models with the goal of avoiding the generation of incorrect SMILES strings is detrimental to learning other chemical properties and that allowing models to generate incorrect molecules, which can be easily removed post hoc, leads to better performing models.
After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.
AI methods can discover new antibiotics but existing methods have limitations. Swanson et al. develop a generative AI model that learns to design molecules that are easy to synthesize. The authors apply the model to design and validate novel antibiotics against the bacterial pathogen Acinetobacter baumannii.
An emerging research area in AI is developing multi-agent capabilities with collections of interacting AI systems. Andrea Soltoggio and colleagues develop a vision for combining such approaches with current edge computing technology and lifelong learning advances. The envisioned network of AI agents could quickly learn new tasks in open-ended applications, with individual AI agents independently learning and contributing to and benefiting from collective knowledge.
As the impacts of AI on everyday life increase, guidelines are needed to ensure ethical deployment and use of this technology. This is even more pressing for technology that interacts with groups that need special protection, such as children. In this Perspective Wang et al. survey the existing AI ethics guidelines with a focus on children’s issues, and provide suggestions for further development.
Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.
Foundation models have transformed artificial intelligence by training on vast amounts of broad unlabelled data. Pai et al. present a foundation model leading to more accurate, efficient and robust cancer imaging biomarkers, especially in use cases with small training datasets.
AI tools such as ChatGPT can provide responses to queries on any topic, but can such large language models accurately ‘write’ molecules as output to our specification? Results now show that models trained on general text can be tweaked with small amounts of chemical data to predict molecular properties, or to design molecules based on a target feature.
Deep learning generative approaches have been used in recent years to discover new molecules with drug-like properties. To improve the performance of such approaches, Yang et al. add chemical binding knowledge to a deep generative framework and demonstrate, including by wet-lab verification, that the method can find valid molecules that successfully bind to target proteins.
Genome-wide association studies allow connecting genomic information with complex traits. Rodrigo Bonazzola et al. develop a framework consisting of several deep learning tools to improve the discoverability of genes that influence specific geometric features of the heart.
Can non-state multinational tech companies counteract the potential democratic deficit in the emerging global governance of AI? We argue that although they may strengthen core values of democracy such as accountability and transparency, they currently lack the right kind of authority to democratize global AI governance.
Visual representations are thought to develop from visual experience and inductive biases. Orhan and Lake show that modern machine learning algorithms can learn visual knowledge from a few hundred hours of longitudinal headcam recordings collected from young children during the course of early development, without strong inductive biases.
This Reusability Report examines a recently published deep learning method PENCIL by Ren et al. for identifying phenotype populations in single-cell data. Cao et al. reproduce here the main results, analyse the sensitivity of the method to model parameters and describe how the method can be used to create a signature for immunotherapy response markers.
Mutations can increase or decrease a protein’s ability to bind to other proteins, but modelling multiple mutations becomes computationally intractable. Lan and colleagues propose an adversarial deep learning architecture to guide the choice of mutations to optimize binding affinities.
One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.
Machine learning methods have made great advances in modelling protein sequences for a variety of downstream tasks. The representation used as input for these models has been primarily the sequence of amino acids. Outeiral and Deane show that using codon sequences instead can improve protein representations and lead to model performance.
Training a machine learning model with multiple tasks can create more-useful representations and achieve better performance than training models for each task separately. In this Perspective, Allenspach et al. summarize and compare multi-task learning methods for computer-aided drug design.
Algorithmic decisions have a history of harming already marginalized populations. In an effort to combat these discriminative patterns, data-driven methods are used to comprehend these patterns, and recently also to identify disadvantaged communities to allocate resources. Huynh et al. analyse one of these tools and show a concerning sensitivity to input parameters that can lead to unintentional biases with substantial financial consequences.
A parameterized physical model that uses unpaired datasets for adaptive holographic imaging was published in Nature Machine Intelligence in 2023. Zhang and colleagues evaluate its performance and extend it to non-perfect optical systems by integrating specific optical response functions.