We have developed a platform of human 3D neural spheroids and bioprinted tissues assembled in multi-well plate formats using iPSC-derived cells optimized for HTS. These models enable the generation of functional, brain region-specific models containing neurons, astrocytes, and microglia. Functional activity is assessed using calcium imaging, fluorescent neurotransmitter biosensors, fiber-based electrophysiology, and additional HTS-compatible endpoints. This talk will describe their applications in pharmacological testing, neurotoxicity assessment, and neurological drug discovery.

We present a new 3D fibrotic scar microphysiological system (MPS) derived directly from the liver of patients with Metabolic Syndrome Associated Hepatosteatitis (MASH). Unlike animal models or iPSC derived MPS, this patient derived MPS enables human disease-state preservation, spatially resolved cell-type interactions, and functional, cell-selective lipid nanoparticle (LNP) mediated testing of therapeutic RNAs within a human fibrotic liver microenvironment. Therefore this patient derived MPS provides a preclinical tool with direct human relevance and high potential for clinical translation.

Our AI/ML platform is pioneering a fundamentally new approach to drug discovery by leveraging advanced transcriptomics to comprehensively understand gene networks and dynamic AI modeling to identify oral cell state-correcting therapeutics that can restore proper cell function. We applied this framework to identify novel druggable nodes to treat myelofibrosis by selectively targeting mutant JAK2 HSPCs while sparing normal hematopoiesis to reduce the anemia risk seen with standard clinical therapies.

We present label-free single-cell buoyant mass profiling as a rapid (<20-minute) phenotypic screening platform for pre-clinical drug discovery. Using a Suspended Microchannel Resonator, we map a biophysical-transcriptomic continuum in resting T cells. Heavier cells exhibit a scalable "translation engine" primed for activation, whereas lighter cells display distinct exhaustion signatures. This stimulation-independent assay provides a novel functional endpoint for evaluating immunomodulators, validating targets, and optimizing next-generation cell therapy manufacturing.

We present an integrated platform for discovering and optimizing VH-based modalities that couples automated, function-first high-throughput screening with AI/ML-guided hit selection and lead optimization. Our approach embeds comprehensive in silico developability profiling early, enabling rapid triage of binders with favorable physicochemical and manufacturability properties. By linking VH discovery to high-throughput functional assays in cell therapy systems, we accelerate identification of translatable, mechanism-driven hits against difficult targets.

The spatial organization of membrane proteins shapes cellular function, yet target discovery largely relies on protein expression alone. We introduce MetaMap, an analytical framework leveraging proximity-informed graph learning to define spatial protein communities across tumor cell surfaces. By integrating these spatial networks with deep learning, we identify "Tumor-Associated Proximity Antigens" (TAPAs) - revealing novel, disease-specific co-targets for precision multispecific therapeutics.

Membrane proteins represent the most drugged protein class yet remain underrepresented in quantitative ligand-binding datasets. Using affinity selection mass spectrometry applied directly to native membrane fractions, we generated standardized dissociation constants (Kd) for ~3,400 ligands across ~400 transmembrane proteins. Comparison with AI-predicted affinities reveals variability in prediction–measurement agreement across targets (R = 0.04–0.68), demonstrating that target-specific calibration is required and establishing experimental ground truth for reliable AI-driven drug discovery.

In this talk, we introduce an interactive multimodal AI co-scientist for drug discovery. The system brings together scientific literature, structural biology, ligand knowledge, molecular visualization, data analysis, reporting, and code execution into a single conversational interface. More broadly, this work suggests a future in which AI co-scientists lower the barrier to complex molecular reasoning, make advanced analysis more widely accessible, and help reshape how discovery science is done.

Drug discovery produces an enormous range of data, from structure to assays to images to omics. Our goal is to bring these modalities together in a single modeling framework. In this talk, I’ll discuss two steps toward that vision: NeuralPLexer, for understanding protein-ligand binding, and Enchant, for learning broad, multimodal representations of molecules and their biological consequences.

Covalent drug discovery is undergoing a renaissance, unlocking targets long considered beyond the reach of conventional approaches. By weaving together AI, chemoproteomics, and quantum mechanics into a single integrated workflow, the Frontier platform—powered by Covalent AI—is purpose-built to turn "undruggable" into actionable, opening the door to therapeutic intervention across the vast majority of the human proteome.

Protein kinase inhibitors represent one of the most promising avenues for cancer therapy. However, drug resistance to kinase inhibitors frequently occurs in cancer. Tumor cells can circumvent kinase inhibitors through kinome reprogramming, a process characterized by system-wide changes in protein kinase networks. Here, we combined kinome profiling and phosphoproteomics to define the fraction of the kinome activated by targeted therapies to rationally predict combination therapies in various cancer models.

DegDig is a high-throughput, target-guided proteomics platform for quantitative profiling of degrader activity across the proteome. It measures multiplexed protein abundance changes after degrader treatment, providing direct readouts of degradation efficiency (Dmax/DC50) and off-target selectivity. Using a 384-well workflow, predefined protein panels, nanoscale TMT labeling, and next-generation mass spectrometry, DegDig enables scalable screening, optimization, and mechanism-of-action studies.