Flexible Flows for Biological Sequence Design
1. FlexFlow reframes discrete flow matching for biological sequences by changing the coupling (forward endpoint pairing) rather than the training objective: a structured, biology-informed coupling uses substitution matrices (e.g., BLOSUM for proteins; JC69/HKY85-style biases for nucleotides) to tilt the source distribution toward evolutionarily plausible neighborhoods.
2. The key idea is to keep the standard token-wise mixture path and CTMC machinery intact, but swap the usual “uninformative” couplings (uniform/masked) with a transition kernel Kγ that encodes preferred substitutions; when Kγ is uniform, the method reduces to the standard uniform coupling.
3. For variable-length generation, FlexFlow builds on Edit Flows by parameterizing reverse-time CTMC rates via edit operations (insertion, deletion, substitution). Instead of treating positions independently, it introduces a shared global latent r that conditions per-position edit decisions, coupling token-level operations through sequence-level context.
4. FlexFlow adds test-time control over edit behavior: operation probabilities are temperature-scaled and modeled with a Dirichlet prior over {ins, sub, del}. By changing Dirichlet concentrations α post-hoc, users can bias generation toward more insertions vs substitutions vs deletions without retraining, effectively acting as an “operation budget controller.”
5. The paper proposes latent classifier-free guidance (CFG) as an alternative to rate-space guidance: it performs CFG by interpolating conditional/unconditional latents (rc and r∅) in continuous space, then uses the guided latent to drive all edit operations jointly—aiming for more globally coherent conditioning than token-wise rate guidance.
6. The latent guidance has a probabilistic interpretation: under Gaussian conditional/unconditional latent encodings and sufficiency assumptions, the guidance direction corresponds to the score of an implicit classifier p(c|r), making the latent interpolation analogous to a gradient ascent step on log p(c|r).
7. Training uses an augmented alignment space with a blank token ε to make edit-based objectives tractable: alignments define edit sequences between endpoints, and a Bregman-divergence-style loss penalizes extraneous rates while rewarding edits that move xt toward x1.
8. DNA enhancer generation (unconditional, length 500) on fly brain and melanoma ATAC-seq datasets: FlexFlow achieves the best Fréchet Biological Distance among compared diffusion/flow baselines at the same sampling budget (100 reverse steps), and ablations indicate combining a frequency-informed prior with structured coupling performs best.
9. Conditional promoter design (human promoters, length 1024) conditioned on transcription initiation profiles: FlexFlow improves MSE of predicted regulatory activity versus prior baselines, with latent guidance outperforming rate guidance (reported 0.022 vs 0.024 MSE at 100 steps), suggesting benefits from global latent steering.
10. A new peptide–MHC II conditional generation benchmark is introduced using eluted ligand data with a strict split where no 9-mer is shared across train/test clusters. On this task, FlexFlow greatly improves a held-out DeepMHCII-based discriminator score (rate guidance 0.58; latent guidance 0.66), while highlighting a quality–diversity tradeoff (latent guidance can improve plausibility while worsening embedding-distance coverage metrics).
📜Paper:
arxiv.org/abs/2606.10543
#ComputationalBiology #GenerativeModels #FlowMatching #DiffusionModels #ProteinDesign #DNADesign #PeptideDesign #MHC #MachineLearning #Bioinformatics