machine-learning

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.

For a week we’ve modeled molecules one at a time — designed, evolved, force-fielded, screened. Today the lens widens: biology is a web of relationships, and machine learning on that web predicts the connections we haven’t drawn. Himmelstein et al. fused biomedical knowledge into Hetionet — ‘47,031 nodes of 11 types and 2,250,197 relationships of 24 types’ — and scored ‘209,168 compound-disease pairs’ for repurposing, ’entirely open.’ Zitnik et al.’s Decagon brought graph neural networks: ‘a new graph convolutional neural network for multirelational link prediction,’ predicting ’the exact side effect’ of a drug pair and ‘outperforming baselines by up to 69%.’ When COVID hit, a network-medicine consensus ranked 6,340 drugs and screened the top ones at a ‘62% success rate, in contrast to the 0.8% hit rate of nonguided screenings’ — and ‘76 of the 77’ hits act through mechanisms ’that cannot be identified using docking-based strategies.’ PrimeKG released an open precision-medicine graph of ‘17,080 diseases with 4,050,249 relationships,’ and TxGNN turned it into ‘a graph foundation model for zero-shot drug repurposing,’ finding candidates ’even for diseases with … no existing drugs,’ with ‘multi-hop’ explanations. As Li, Huang & Zitnik put it, ‘graphs are universal descriptors of systems of interacting elements.’ Reasoning over the web of biology.

Antibiotic resistance is outrunning the discovery pipeline — so researchers taught machines to look where humans can’t. Stokes et al. ’trained a deep neural network capable of predicting molecules with antibacterial activity’ and surfaced halicin, ‘structurally divergent from conventional antibiotics.’ Liu et al. screened ~7,500 molecules and found abaucin, with ’narrow-spectrum activity against A. baumannii,’ a priority Gram-negative pathogen. Wong et al. made the black box talk — graph neural networks predicting activity and cytotoxicity for ‘12,076,365 compounds’ and yielding a new structural class ‘selective against methicillin-resistant S. aureus.’ Das et al. designed antimicrobials outright with ‘deep generative models and molecular dynamics simulations.’ And two teams went mining nature’s own arsenal: 181 of 216 gut-microbiome peptides proved active, and AMPSphere catalogued ‘863,498 non-redundant peptides’ from the global microbiome — an open resource. AI widens where we look for medicine.

To simulate a molecule you need the forces on every atom. The gold standard, quantum mechanics, is ‘computationally demanding … making long simulations of large systems unfeasible.’ The workaround is a neural network that learns the potential-energy surface itself. Behler & Parrinello introduced one ‘several orders of magnitude faster than DFT’; ANI-1 delivered ‘DFT accuracy at force field computational cost,’ transferable across organic chemical space; SchNet showed deep nets are ‘ideally suitable for representing quantum-mechanical interactions.’ DeePMD made it scale — quantum-accurate molecular dynamics ‘at a cost that scales linearly with system size’ — and NequIP baked in the symmetries of 3D space, ‘challenging the widely held belief that deep neural networks require massive training sets’ by learning from ‘up to three orders of magnitude fewer training data.’ As Unke et al. put it, the aim is ’to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs.’ It’s the physics engine a virtual cell will run on.

Yesterday we read the genome; last Friday we designed proteins from scratch. Today’s verb is evolve — using machine learning to improve the proteins nature already gave us. Directed evolution won a Nobel; adding ML lets it ‘predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics.’ DeepSequence showed you can score mutations ‘in an unsupervised manner solely on the basis of sequence information,’ beating prior methods across deep mutational scans. UniRep distilled proteins into a representation that’s ‘structurally, evolutionarily and biophysically grounded,’ buying ’two orders of magnitude efficiency improvement.’ Low-N built ‘an accurate virtual fitness landscape’ from ‘as few as 24 functionally assayed mutant sequences’ and screened ten million in silico. Hsu et al. showed a plain ridge regression ‘is competitive with, and on average outperforms more sophisticated methods’ — and preached ’the importance of systematic evaluations and sufficient baselines.’ And at the bench, ML-guided evolution engineered a new-to-nature enzyme to ‘93% and 79% ee.’ Design–build–test–learn, made real.

We’re excited to introduce Linh, a Researcher with dual expertise in molecular biology and machine learning. His arrival opens new avenues for AICell Lab in automated cell manipulation and imaging.

The AICell Lab is thrilled to welcome our first PhD student, Songtao Cheng, this coming October. His multidisciplinary expertise in machine learning, lab automation, and biological wet lab skills will be instrumental in advancing our research projects.