Publications
AC699 is a novel, orally bioavailable chimeric estrogen receptor-α (ERα) degrader that induces proteasome-dependent ERα degradation via cereblon E3 ligase recruitment. Here we report findings from a first-in-human, phase 1, dose-escalation study of once-daily AC699 (100–600 mg) in patients with heavily-pretreated, locally-advanced/metastatic ER-positive/HER2-negative breast cancer (ClinicalTrials.gov Identifier: NCT05654532). Primary objectives are to assess dose-limiting toxicities and treatment-emergent adverse events (TEAEs). Secondary objectives are to evaluate pharmacokinetics, objective response rate (ORR), clinical…
PI3Kα is a critical oncogenic driver in a broad range of solid tumors, including breast cancer, where activating mutations and pathway hyperactivation are frequently observed. Although two PI3Kα inhibitors have recently been approved by the FDA for breast cancer treatment, their clinical utility is significantly constrained by dose-limiting, on-target toxicities due to wild-type PI3Kα inhibition, most notably insulin resistance and hyperglycemia. Antibody-drug conjugates (ADCs) offer a compelling strategy to enhance…
Prostate cancer (PCa) is primarily driven by aberrant Androgen Receptor (AR) signaling. Although there has been substantial advancement in antiandrogen therapies, resistance to these treatments remains a significant obstacle, often marked by continuous or enhanced AR signaling in resistant tumors. While the dysregulation of the ubiquitination-based protein degradation process is instrumental in the accumulation of oncogenic proteins, including AR, the molecular mechanism of ubiquitination-driven AR degradation remains largely undefined. We…
We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably. The system was trained on a processed patent database of over 3 million organic reactions. Experimental reactions were atom-mapped, clustered, and extracted into reaction templates. We then trained a graph convolutional neural network-based one-step reaction proposer using template embeddings and developed a guiding algorithm on the directed acyclic graph…
We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to…