seqme: a Python library for evaluating biological sequence design from generative models

ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.

ScrambleBench provides a holistic medicinal chemistry-oriented framework that identifies methodological strengths, limitations, and opportunities for future model development and highlights the importance of evaluating chemical diversity explicitly and using the recently proposed metrics such as Hamiltonian Diversity (HamDiv) which assess both quantity and dissimilarity of a molecular set.

DeepGeSeq: deep learning library for genomic sequence modeling and analysis

Abstract Motivation Deep learning methods have demonstrated significant potential in genomics, enabling broad applications such as sequence activity prediction, regulatory rule identification, and variant effect quantification. However, their widespread adoption is often hindered by the steep computational learning curve required for model construction, training, and downstream biological interpretation. Here, we introduce DeepGeSeq, a user-friendly Deep-learning library tailored for Genomic Sequence modeling and analysis. Results By integrating state-of-the-art architectural modules, DeepGeSeq streamlines the entire deep learning workflow, requiring minimal user input via a simple configuration file and an intuitive agentic skill. We comprehensively validate the efficacy of DeepGeSeq through diverse case studies, encompassing pipeline verification using synthetic datasets, the reproduction and application of established models, and model fine-tuning coupled with biological interpretation on user-defined data. Furthermore, we demonstrate DeepGeSeq’s versatility in domain-specific applications, including single-cell ATAC-seq modeling for cell-type clustering, and MPRA data modeling coupled with in silico saturation mutagenesis to dissect cis-regulatory elements. Ultimately, DeepGeSeq bridges the gap between computational complexity and biological discovery, providing an accessible resource that facilitates the development and broad application of deep learning methods in genomics research. Availability and implementation https://github.com/JiaqiLi1024/DeepGeSeq.

AI Guided Simulated Annealing for Automated Gene Editing Design

Designing effective gene and mRNA sequences is a difficult optimisation problem because the number of possible nucleotide combinations grows extremely quickly with sequence length. Traditional optimisation methods such as simulated annealing are well suited to exploring these large search spaces, but their performance depends heavily on the quality of the scoring function used to evaluate candidate sequences. Hand crafted scoring rules are often slow to compute and cannot easily adapt to different biological contexts or patient specific constraints. This project presents an AI-guided simulated annealing framework for automated gene sequence design using two approaches. The first replaces fixed rule-based scoring with an adaptive model evaluating candidates using biological reference data and patient-specific information. By adjusting biological trait importance based on age, disease background, and treatment goals, the scoring model dynamically changes sequence evaluation without modifying the optimization algorithm. The second approach employs Gradient Boosting Regression on CRISPR guide RNA sequences with extracted biological features including GC content, positional nucleotides, and sequence complexity metrics. This model learns from validated literature guides, providing interpretable, deterministic scoring while maintaining adaptability. The framework is designed to support long running and repeated simulated annealing searches with minimal human intervention. Sequence evaluation is decoupled from the optimisation engine so that scoring models and reference databases can be updated as new experimental or clinical data becomes available. This allows the same optimisation pipeline to be reused across different applications such as vaccine design, cancer related gene targets or personalised therapies. By combining a fast native optimisation core with an adaptive and context aware evaluation model, this work demonstrates a flexible approach to large scale gene sequence optimisation. The proposed system highlights how AI driven scoring can improve the practicality of heuristic search methods and move sequence design closer to personalised and data driven biomedical applications.

Efficient exploration of sequence space enables rapid generation of functional genome editors

WASP: a pipeline for functional annotation prediction based on AlphaFold structural models

WASP highlights how structural homology can systematically discover annotations missed by sequence-based approaches, predicting protein functions from AlphaFold structures using network-based structural homology and filling metabolic model gaps by mapping 75-100% of orphan reactions.

Simulating Gene Regulatory Networks for Validation of scRNA-seq Data Alignment Methods