An antibody binds its target through six tiny hypervariable loops — and for decades the only way to improve one was to make thousands and screen them. Today’s digest is about the deep-learning models that learned to read, write, and refine antibodies. Ruffolo et al.’s DeepAb tackled structure with ‘interpretable deep learning,’ then IgFold scaled it — ‘a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks,’ predicting structures ‘of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s).’ Olsen et al.’s AbLang showed that for ‘antibody specific problems … a model trained solely on antibodies may be more powerful,’ restoring missing residues better than ’the general protein language model ESM-1b.’ Shuai et al.’s IgLM turned it generative, ‘a deep generative language model for creating synthetic antibody libraries’ via ’text-infilling.’ Hie et al. proved general PLMs ‘can efficiently evolve human antibodies … despite providing the model with no information about the target antigen,’ improving affinities ‘up to 160-fold’ with ‘20 or fewer variants.’ And Mason et al. attacked the screening bottleneck, ‘predicting antigen specificity from antibody sequence via deep learning.’ Reading, writing, and refining the immune system’s keys.

Before you can compare two images, track a cell, or stack tissue slices into a 3D atlas, you have to line them up — and for years that meant a slow optimization run for every single pair. This week we read molecules; today we return to imaging’s quiet, load-bearing step. Balakrishnan et al. built VoxelMorph, reframing registration ‘as a function that maps an input image pair to a deformation field,’ computed ‘orders of magnitude faster’ than classical methods. De Vos et al.’s DLIR trained that function ‘unsupervised … by exploiting image similarity,’ since ‘obtaining example registrations is not trivial.’ Kim et al.’s CycleMorph added ‘cycle consistency’ as ‘an implicit regularization to preserve topology during the deformation.’ Chen et al.’s TransMorph brought transformers — whose ‘substantially larger receptive field enables a more precise comprehension of the spatial correspondence’ — with diffeomorphic and ‘well-calibrated registration uncertainty’ variants. Hoffmann et al.’s SynthMorph learned ‘without acquired imaging data,’ training on synthetic shapes to become contrast-agnostic. And Zeira et al.’s PASTE carried it into spatial omics, aligning tissue slices ‘using an optimal transport formulation’ to build ‘a stacked 3D alignment of a tissue.’ Lining images up, learned.

The genome isn’t a string — it’s a folded 3D object, and where a gene sits in that fold decides which enhancers can reach it. This week we read the sequence; today we watch it fold. Fudenberg et al. built Akita, ‘a convolutional neural network … that accurately predicts genome folding from DNA sequence alone,’ learning ‘an orientation-specific grammar for CTCF binding sites.’ Schwessinger et al.’s DeepC used ‘megabase-scale transfer learning’ to ‘predict the impact of both large-scale structural and single base-pair variations.’ Zhou’s Orca went multiscale — ‘from kilobase to whole-chromosome scale’ — recapitulating variant effects from ‘300 bp to 90 Mb.’ Tan et al.’s C.Origami does ‘de novo prediction of cell-type-specific chromatin organization,’ powering ‘high-throughput in silico genetic screening’ in leukemia vs normal T cells. Yang et al.’s Epiphany predicts ‘cell-type-specific Hi-C contact maps from widely available epigenomic tracks,’ with a GAN ’to encourage contact map realism.’ And GraphReg closes the loop, using ‘graph attention networks to exploit the connectivity of distal elements up to 2 Mb away’ to predict gene expression — validated by ‘CRISPRi-FlowFISH and TAP-seq.’ Decoding genome function from sequence, through structure.

A cell’s fate isn’t written only by its own genome — it’s shaped by the signals arriving from its neighbors. For a week we’ve zoomed in on single molecules; today we listen to the conversations between whole cells, reconstructed from the same sequencing data. Efremova et al. built CellPhoneDB, ‘a novel repository of ligands, receptors and their interactions’ whose database ’takes into account the subunit architecture of both ligands and receptors,’ with ‘a statistical framework that predicts enriched cellular interactions.’ Jin et al.’s CellChat can ‘quantitatively infer and analyze intercellular communication networks,’ predicting ‘major signaling inputs and outputs’ via ‘manifold learning.’ NicheNet went further — ’linking ligands to target genes’ by folding in ‘prior knowledge on signaling and gene regulatory networks’ to find ‘active ligands and their gene regulatory effects.’ Then space returned: Cang & Nie’s SpaOTsc uses ‘structured optimal transport’ so ‘cell-cell communications are… obtained by optimally transporting signal senders to target signal receivers in space,’ and COMMOT scaled it, ‘account[ing] for the competition between different ligand and receptor species as well as spatial distances.’ Finally, NCEM brought ‘graph neural networks that estimate the effects of niche composition on gene expression.’ A tissue, it turns out, is a conversation.

The COVID vaccines made one thing obvious: mRNA is a medicine you can design — and the design problem is machine learning. Sample et al. paired ‘polysome profiling of a library of 280,000 randomized 5′ untranslated regions (UTRs) with deep learning,’ then used the model ’to engineer new 5′ UTRs that accurately direct specified levels of ribosome loading’ — extensible to ‘chemically modified RNA … for applications in mRNA therapeutics.’ Karollus et al. added ‘frame pooling, a novel neural network operation,’ to predict ribosome load ‘for 5′UTR of any length,’ and read a beta-thalassemia HBB variant. Wayment-Steele et al. attacked shelf-life: mRNA hydrolysis is beaten by designing structure to lower the ‘average unpaired probability,’ yielding ‘superfolder’ mRNAs with ‘≥two-fold’ half-life. LinearDesign faced ‘around 2.4 × 10^632 candidate mRNA sequences for the SARS-CoV-2 spike protein’ and, reframing it ‘as a lattice parsing problem,’ found an optimum ‘in just 11 minutes,’ raising ‘antibody titre by up to 128 times in mice.’ UTR-LM brought a ‘5′ UTR language model’ whose designs beat a therapeutic baseline by ‘32.5%.’ And Angenent-Mari et al. showed RNA that computes — deep nets predicting toehold-switch function at ‘R2 = 0.43–0.70’ vs ‘0.04–0.15’ for thermodynamic models. Designing the message, base by base.

A computational postdoc in the AICell Lab developing AI models that represent, predict and simulate the human cell — part of SciLifeLab’s AlphaCell program. Deadline 3 August 2026.

A biology-first postdoc in the AICell Lab for a researcher who wants to pursue curiosity-driven cell & molecular biology in a team where AI and automation are everyday tools. Deadline 3 August 2026.