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Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 Mpro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. 

BackgroundThe advancement of sequencing technology has led to a rapid increase in the amount of DNA and protein sequence data; consequently, the size of genomic and proteomic databases is constantly growing. As a result, database searches need to be continually updated to account for the new data being added. However, continually re-searching the entire existing dataset wastes resources. Incremental database search can address this problem. MethodsOne recently introduced incremental search method is iBlast, which wraps the BLAST sequence search method with an algorithm to reuse previously processed data and thereby increase search efficiency. The iBlast wrapper, however, must be generalized to support better performing DNA/protein sequence search methods that have been developed, namely MMseqs2 and Diamond. To address this need, we propose iSeqsSearch, which extends iBlast by incorporating support for MMseqs2 (iMMseqs2) and Diamond (iDiamond), thereby providing a more generalized and broadly effective incremental search framework. Moreover, the previously published iBlast wrapper has to be revised to be more robust and usable by the general community. ResultsiMMseqs2 and iDiamond, which apply the incremental approach, perform nearly identical to MMseqs2 and Diamond. Notably, when comparing ranking comparison methods such as the Pearson correlation, we observe a high concordance of over 0.9, indicating similar results. Moreover, in some cases, our incremental approach, iSeqsSearch, which extends the iBlast merge function to iMMseqs2 and iDiamond, provides more hits compared to the conventional MMseqs2 and Diamond methods. ConclusionThe incremental approach using iMMseqs2 and iDiamond demonstrates efficiency in terms of reusing previously processed data while maintaining high accuracy and concordance in search results. This method can reduce resource waste in continually growing genomic and proteomic database searches. The sample codes and data are available at GitHub and Zenodo (https://github.com/EESI/Incremental-Protein-Search; DOI:10.5281/zenodo.14675319). 

Analyzing the origin and diversity of numerous genomic sequences, such as those sampled from the human microbiome, is an important first step in genomic analysis. The use of normalized compression distance (NCD) has demonstrated capabilities in the field of text classification as a low-resource alternative to deep neural networks (DNNs) by leveraging compression algorithms to approximate Kolmogorov information distance. In an effort to apply this technique toward genomics tasks akin to tools such as Many-against-Many sequence searching (MMseqs) and Kraken2, we have explored the use of agzip-based NCD combination in both gene labeling of open reading frames (ORFs) and taxonomic classification of short reads. Our implementation achieved 0.89 accuracy and 0.88 macro-F1 on human gene classification, surpassing similar NCD-based approaches. In prokaryotic gene labeling tasks, NCD shows superior classification accuracy to traditional alignment or exact-match tools in out-of-distribution settings, while also outperforming comparable sequence-embedding methods in in-distribution classification. However, the computational complexity of O(MN) (in standard big-O notation, where M and N denote the sizes of the training and test databases, respectively) constrains scalability to very large datasets, though these findings nonetheless demonstrate that compression-based approaches provide an effective alternative for genomic sequence classification, particularly in low-data environments. 

Chain-transfer ring-opening metathesis polymerization (CT-ROMP) previously provided a route to carboxytelechelic polyethylene (PE) of controlled molecular weight; however, the incorporation of oligomeric PE into segmented copolymers remains unexplored. Herein, CT-ROMP afforded carboxytelechelic polycyclooctene segments, and subsequent reduction generated well-defined carboxytelechelic PE with M n = 3900 g mol −1 . Solvent-free melt polycondensation of neopentyl glycol and adipic acid with varying wt% telechelic PE oligomers yielded mechanically durable segmented copolyesters. The thermal and thermomechanical properties of the segmented copolyesters correlated with PE segment content, and high PE content copolymers exhibited remarkably similar morphologies and thermomechanical performance to conventional HDPE. The segmented copolyesters displayed advantageous physical properties while introducing susceptibility to chemo- and bio-catalytic depolymerization through periodic ester linkages, thus providing valuable fundamental understanding of an alternative route to HDPE.