AI-Driven Forensic Science: Decoding Complex Chemical and Biological Mixtures with Advanced Models

Forensic science plays a vital role in identifying, characterizing, and quantifying physical and biological traces recovered from crime scenes — a task that frequently involves analyzing highly complex, degraded, or variable mixtures. Traditional analytical and statistical methods, while robust, often struggle to resolve overlapping chemical signals, separate low-template DNA from multiple contributors, or rapidly process vast, multi-dimensional datasets. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, are transforming this analytical landscape by offering sophisticated tools capable of extracting meaningful patterns from noisy, complex forensic data. By transitioning from classical statistics to advanced predictive modelling, researchers can now tackle previously intractable decoding challenges. This collection serves as a shared computational forum uniting breakthroughs across the chemical, biological, and digital sub-disciplines of forensic investigation.

The reliable deconvolution and interpretation of complex mixtures — whether chemical, biological, or digital — remains one of forensic science’s most persistent and high-stakes hurdles. Conventional analytical methods are often pushed to their limits when handling trace analytes, degraded materials, or heavily convoluted multi-contributor samples. This Research Topic seeks to bridge the gap between classical forensics and modern data science by showcasing advanced, tailor-made AI-driven models. The objective is to promote novel computational frameworks that can resolve overlapping chemical signals, enhance minor DNA contributor detection, improve predictive modelling accuracy, and automate routine forensic analyses. By framing mixture deconvolution as a unified computational challenge, this collection aims to facilitate rapid, reproducible, and legally robust interpretations of forensic evidence — establishing a benchmark for the ethical and transparent integration of AI in forensic laboratories and shaping the future of criminal investigation and judicial practice.

We welcome Original Research, Review, Mini Review, Perspective, Hypothesis & Theory, and FAIR² Data Report articles on themes including, but not limited to:
• Chemical Mixture Analysis: Machine learning and deep learning models for deconvoluting complex chromatograms, mass spectra, Raman or infrared spectra, and volatile organic compound (VOC) profiles (e.g., ignitable liquid residues, explosives, illicit substances).
• Biological Mixture Analysis: AI-assisted deconvolution of complex, low-template, or degraded multi-contributor DNA profiles (probabilistic genotyping).
• Forensic Intelligence & Phenotyping: Advanced algorithms for predicting externally visible characteristics (EVCs), biogeographical ancestry, and chronological age from genomic data.
• Forensic Multi-Omics: Machine learning applications leveraging transcriptomics, epigenomics (DNA methylation), and microbiomics for tissue identification, post-mortem interval (PMI) estimation, or geolocation.
• Validation, Explainability & Courtroom Admissibility: Benchmarking AI tools against classical standards, addressing the “black box” challenge, and establishing frameworks for legal acceptability.