ArticleBMC bioinformatics2021
Fast activation maximization for molecular sequence design.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- Gradient-based optimization for mRNA sequence design.Bioinformatics (Oxford, England) · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Sequence design for three-dimensional genome folding using Akita Semifreddo.bioRxiv : the preprint server for biology · 2026Article
- Article
- Protein-inducible ribosomal frameshifting enables programmable translational control for genetic circuit design in Escherichia coli.Journal of biological engineering · 2026Article
- inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences.Bioinformatics (Oxford, England) · 2026Article
- Reimagining the Synthetic Biology DBTL Cycle with Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.bioRxiv : the preprint server for biology · 2025Article
- Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity.Cell systems · 2025Article
- A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.bioRxiv : the preprint server for biology · 2025Article
- Article
- Inverse design of metal-organic frameworks using deep dreaming approaches.Nature communications · 2025Article
- The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.Developmental cell · 2025Article
- Using GNN property predictors as molecule generators.Nature communications · 2025Article
- Design of overlapping genes using deep generative models of protein sequences.bioRxiv : the preprint server for biology · 2025Article
- Polygraph: a software framework for the systematic assessment of synthetic regulatory DNA elements.Genome biology · 2025Article
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
- Multi-objective computational optimization of human 5' UTR sequences.Briefings in bioinformatics · 2025Article
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2 authors.
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Abstract
backgroundOptimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence.
resultsHere, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp's capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor.
conclusionsFast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.
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