ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillation.

Recent studies showed that the likelihood of drug approval can be predicted with clinical data and structure information of drug using computational approaches. Predicting the likelihood of drug approval can be innovative and of high impact. However, models that leverage clinical data are applicable only in clinical stages, which is not very practical. Prioritizing drug candidates and early-stage decision-making in the de novo drug development process is crucial in pharmaceutical research to optimize resource allocation. For early-stage decision-making, we need a computational model that uses only chemical structures. This seemingly impossible task may utilize the predictive power with multi-modal features including clinical data. In this work, we introduce ChemAP (Chemical structure-based drug Approval Predictor), a novel deep learning scheme for drug approval prediction in the early-stage drug discovery phase. ChemAP aims to enhance the possibility of early-stage decision-making by enriching semantic knowledge to fill in the gap between multi-modal and single-modal chemical spaces through knowledge distillation techniques. This approach facilitates the effective construction of chemical space solely from chemical structure data, guided by multi-modal knowledge related to efficacy, such as clinical trials and patents of drugs. In this study, ChemAP achieved state-of-the-art performance, outperforming both traditional machine learning and deep learning models in drug approval prediction, with AUROC and AUPRC scores of 0.782 and 0.842 respectively on the drug approval benchmark dataset. Additionally, we demonstrated its generalizability by outperforming baseline models on a recent external dataset, which included drugs from the 2023 FDA-approved list and the 2024 clinical trial failure drug list, achieving AUROC and AUPRC scores of 0.694 and 0.851. These results demonstrate that ChemAP is an effective method in predicting drug approval only with chemical structure information of drug so that decision-making can be done at the early stages of drug development process. To the best of our knowledge, our work is the first of its kind to show that prediction of drug approval is possible only with structure information of drug by defining the chemical space of approved and unapproved drugs using deep learning technology.

Exploring chemical space for lead identification by propagating on chemical similarity network.

Motivation: Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results: In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.

Characterization of radiation-resistance mechanism in Spirosoma montaniterrae DY10T in terms of transcriptional regulatory system.

To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10[Formula: see text] and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.

SREBP1c-PARP1 axis tunes anti-senescence activity of adipocytes and ameliorates metabolic imbalance in obesity.

Emerging evidence indicates that the accretion of senescent cells is linked to metabolic disorders. However, the underlying mechanisms and metabolic consequences of cellular senescence in obesity remain obscure. In this study, we found that obese adipocytes are senescence-susceptible cells accompanied with genome instability. Additionally, we discovered that SREBP1c may play a key role in genome stability and senescence in adipocytes by modulating DNA-damage responses. Unexpectedly, SREBP1c interacted with PARP1 and potentiated PARP1 activity during DNA repair, independent of its canonical lipogenic function. The genetic depletion of SREBP1c accelerated adipocyte senescence, leading to immune cell recruitment into obese adipose tissue. These deleterious effects provoked unhealthy adipose tissue remodeling and insulin resistance in obesity. In contrast, the elimination of senescent adipocytes alleviated adipose tissue inflammation and improved insulin resistance. These findings revealed distinctive roles of SREBP1c-PARP1 axis in the regulation of adipocyte senescence and will help decipher the metabolic significance of senescence in obesity.

Spatial Regulation of Reactive Oxygen Species via G6PD in Brown Adipocytes Supports Thermogenic Function.

Reactive oxygen species (ROS) are associated with various roles of brown adipocytes. Glucose-6-phosphate dehydrogenase (G6PD) controls cellular redox potentials by producing NADPH. Although G6PD upregulates cellular ROS levels in white adipocytes, the roles of G6PD in brown adipocytes remain elusive. Here, we found that G6PD defect in brown adipocytes impaired thermogenic function through excessive cytosolic ROS accumulation. Upon cold exposure, G6PD-deficient mutant (G6PDmut) mice exhibited cold intolerance and downregulated thermogenic gene expression in brown adipose tissue (BAT). In addition, G6PD-deficient brown adipocytes had increased cytosolic ROS levels, leading to extracellular signal-regulated kinase (ERK) activation. In BAT of G6PDmut mice, administration of antioxidant restored the thermogenic activity by potentiating thermogenic gene expression and relieving ERK activation. Consistently, body temperature and thermogenic execution were rescued by ERK inhibition in cold-exposed G6PDmut mice. Taken together, these data suggest that G6PD in brown adipocytes would protect against cytosolic oxidative stress, leading to cold-induced thermogenesis.

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF-TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF-TG relations where a module of TF is regulating a module of TGs upon specific stress.