The estrogen receptor α (ERα) is an important biological target mediating 17β-estradiol driven breast cancer (BC) development. Aiming to develop innovative drugs against BC, either wild-type or mutated ligand-ERα complexes were used as source data to build structure-based 3-D pharmacophore and 3-D QSAR models, afterward used as tools for the virtual screening of National Cancer Institute datasets and hit-to-lead optimization. The procedure identified Brefeldin A (BFA) as hit, then structurally optimized toward twelve new derivatives whose anticancer activity was confirmed both in vitro and in vivo. Compounds as SERMs showed picomolar to low nanomolar potencies against ERα and were then investigated as antiproliferative agents against BC cell lines, as stimulators of p53 expression, as well as BC cell cycle arrest agents. Most active leads were finally profiled upon administration to female Wistar rats with pre-induced BC, after which 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ-2, and 3DPQ-1 represent potential candidates for BC therapy.

Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT_2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor level, using molecular docking and three-dimensional quantitative structure–activity relationship (3-D QSAR) studies. The bioactive conformations of cathinones were modeled by AutoDock Vina and were used to build structure-based (SB) 3-D QSAR models using the Open3DQSAR engine. Graphical inspection of the results led to the depiction of a 3-D structure analysis-activity relationship (SAR) scheme that could be used as a guideline for molecular determinants by which any untested cathinone molecule can be predicted as a potential 5-HT_2AR binder prior to experimental evaluation. The obtained models, which showed a good agreement with the chemical properties of co-crystallized 5-HT_2AR ligands, proved to be valuable for future virtual screening campaigns to recognize unused cathinones and similar compounds, such as 5-HT_2AR ligands, minimizing both time and financial resources for the characterization of their psychedelic effects.

Computational blind challenges are essential for advancing drug discovery, especially in research areas with limited public data, such as absorption, distribution, metabolism, excretion, toxicity (ADMET) prediction. In this paper, we present the results of our latest community blind challenge, organized in collaboration with Expansion Therapeutics (ExpansionRx). Participants were tasked with predicting nine distinct ADMET endpoints routinely evaluated during lead optimization of compounds in drug discovery programs. Following unprecedented engagement from the global community, this challenge provided valuable insights into the state of the art in machine learning for ADMET modeling. Evaluating submissions against a realistic, challenging dataset from active ExpansionRx drug discovery programs revealed that message-passing neural networks and chemical foundation models dominated among the top performers. Furthermore, the results highlighted the power of proprietary industry datasets and a critical need to optimize predictive workflows for different endpoints independently via multitasking and task-affinity grouping. We also demonstrate how hosting our open-source blind challenge infrastructure on a flexible platform like Hugging Face was pivotal for driving community engagement and visibility. Ultimately, blind challenges supported by robust infrastructure, widespread dissemination, and rigorous post-challenge analysis are fundamental to maximizing the utility of public data releases. We anticipate that these community benchmarks will continue to drive the advancement of ADMET models, enabling their deployment in both early- and late-stage drug discovery.