Mastering the transcriptome with artificial intelligence
Chemo-transcriptomics at scale
The transcriptome occupies a fundamental role as a bedrock biological modality and a vital link in the chain of information of any biological system. At Transcripta Bio, we leverage an omics-first drug discovery approach through the lens of gene expression. Utilizing deep sequencing, scientists capture a comprehensive snapshot of the ongoing dynamic regulatory processes that arise in response to stimuli. These expression quantities unlock several key discovery components, allowing us to simultaneously identify novel targets, uncover enriched disease pathways, and gain insights into regulatory networks. With the wealth of functional knowledge available to us, the hundreds of thousands of data points generated from a single experiment can be leveraged to derive novel insights into the roles that individual genes play in diseases-related biological processes.
Transcripta’s rapid-screening Discovery platform performs high-throughput RNA-Seq assays to screen thousands of unique molecules and quantify their individual effects on a variety of cell types. The resulting millions of perturbations then become training data for our proprietary Conductor AI suite, which predicts gene expression directly from the molecular structure of novel uncharacterized compounds. By unleashing Conductor on massive unlabeled chemical spaces, we are able to discover novel chemo-transcriptomic relationships that:
- Discover new molecular mechanisms that demonstrate improved efficacy
- Navigate off-target effects to improve safety profiles
- Identify compounds with improved target specificity.
Virtual screening for PDE2A downregulation
In an application of Conductor AI’s predictive capabilities, we demonstrate targeted downregulation of PDE2A. Phosphodiesterase type 2A (PDE2A) regulates mitochondria morphology and apoptotic cell death via local modulation of cAMP/PKA signaling, is predominantly expressed in the forebrain, and is known to affect cognitive processes. Inhibition of PDE2A has been associated with improved behavior symptoms in autism spectrum disorder and better stroke recovery via increased neuroregeneration.
With conventional drug-discovery approaches, targeting a specific protein for inhibition is often a tedious and unpredictable endeavor, typically yielding limited success in identifying molecules with the desired specificity and efficacy. In the case of PDE2A, this challenge is exacerbated due to the presence of numerous other PDE genes, making selectivity crucial to avoid interfering with the remaining phosphodiesterases. Our Conductor AI platform, trained on our extensive proprietary dataset, navigates this barrier by predicting the high-dimensional transcriptomic response of any given molecular structure, including 23 different PDE genes. This allows us to perform high-throughput virtual screening of billions of compounds using massive chemical catalogs, enabling the discovery of small molecules that are potent and specific PDE2A downregulators.
In silico activity and downstream experimental validation
Utilizing a billion-scale chemical catalog of novel synthesizable compounds as a search space, Conductor identified a small cohort of molecules that were predicted to induce inhibitory effects on PDE2A. Following the synthesis of these putative actives, in vitro validation using bulk RNA-seq in glutamatergic neurons confirmed their efficacy, demonstrating dose-dependent PDE2A downregulation. Concentration-response curves for two of these chemical inhibitors, shown below, depict the transcript log-fold change relative to baseline levels, illustrating the inhibitory effect.
Translatability from transcript abundance to protein product is a key criterion for any successful drug program. We find that applying the two compounds significantly decreases protein abundance when quantified with protein immunoblotting.
Our roadmap
Going forward, we are poised to significantly enhance our modeling capabilities and uncover innovative therapeutic opportunities as we continue to scale data generation both in the number of screened molecules and in the cell types we screen. Screening molecules strategically selected to expose therapeutic opportunities in unexplored chemical space will significantly improve the precision and scope of our predictive models. Similarly, expanding the diversity of disease-relevant cell types we screen uncovers opportunities in more therapeutic areas while simultaneously facilitating cross–cell type modeling of transcriptomic activity.
In addition to growing our experiment dataset, we are deeply invested in broadening Transcripta’s AI Research Program to derive new quantitative and computational techniques that push the boundaries of biological data modeling in areas such as non-parametric modeling of complex interacting systems, causal modeling of noisy high-dimensional data-generating processes, and multimodal representation learning. Additionally, our experiments with molecular generative models, like NVIDIA’s MolMIM, for molecular property refinement and lead optimization have shown there is promise for profound progress in molecular representation and generation, especially in concert with transcriptomic data.
We look forward to sharing our progress as we continue to grow Conductor’s capability to learn from and predict drug responses across different cellular contexts, broadening the therapeutic areas that we can address and furthering our goal of rapid drug discovery for transformative patient impact.
At Transcripta, we are charting a faster path in drug discovery to create better lives for people around the world. If this interests you and you’d like to collaborate, please contact partnerships@transcriptabio.com.
