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Dr. Bryan Hernandez of the University of the Philippines – Diliman College of Science’s Institute of Mathematics (UPD-CS IM), along with Patrick Vincent Lubenia and Dr. Eduardo Mendoza of the Center for Natural Science and Environmental Research, De La Salle University, developed the Common Species Embedded Networks (CSEN) analysis.

The CSEN analysis compares two reaction networks of the same or similar systems by focusing on their common species, such as proteins or chemicals, to identify similarities and differences.

“The method works by first isolating the networks ‘embedded’ within the models that involve only the common species. For the reactions that are not identical, the method checks for ‘transformations’—mathematical links that can explain how one reaction set might induce equivalence between the systems with the underlying embedded networks,” Dr. Hernandez explained.

Their group compared existing reaction networks associated with Wnt signaling models they had identified, including those by Lee, Schmitz, MacLean, and Feinberg. Through the CSEN analysis, the mathematicians found that some existing models are strongly similar because their embedded networks are connected through specific transformations. Conversely, the analysis can also reveal when no comparable relationship exists due to the absence of such transformations.

Dr. Hernandez shared the team’s inspiration behind creating the CSEN analysis. “In the field of systems biology, different researchers often propose different reaction networks to describe the same biological process. Historically, it has also been difficult to compare these models because they are often treated as entirely separate entities, utilizing different sets of variables and reactions.”

The CSEN analysis addresses this difficulty by emphasizing two key pillars: network embedding, which narrows complex networks to focus only on the species shared between models; and structural comparisons, in which the embedded networks are analyzed to determine whether one model is a more complex version of another or if they are “proximately equivalent” through mathematical transformations.

“Traditional approaches often discriminate between models based on specific properties, such as whether they have one long-term state (mono-stationarity) or the capacity for multiple long-term states (multi-stationarity). CSEN is different because it looks at the underlying structure and dynamical equivalence,” Dr. Hernandez added.

The CSEN analysis was particularly insightful for Wnt signaling, revealing that certain models—previously thought to differ fundamentally due to their stability properties—are actually structurally very similar.

According to Dr. Hernandez, the CSEN analysis is a general mathematical tool that can be applied to models in systems beyond Wnt signaling. “While we demonstrated its use with Wnt signaling, it can be applied to any system represented by reaction networks. This includes other biological pathways, such as insulin signaling or metabolism, as well as potentially non-biological networks such as chemical engineering processes or ecological models.”

The CSEN analysis can be vital in refining models in biological and chemical systems by identifying which parts of a model—or which models—are unique and which are redundant. This method can highlight robust targets: if many models, despite their differences, agree that a specific interaction drives a disease, that interaction becomes a more reliable and robust therapeutic target.

Their research, “Embedding-based comparison of reaction networks of Wnt signaling,” is included in MATCH Communications in Mathematical and in Computer Chemistry, an open-access journal that publishes papers of original research as well as reviews on chemically important mathematical results and non-routine applications of mathematical techniques to chemical problems.

References:

Hernandez, B. S., Lubenia, P. V., & Mendoza, E. R. (2024). Embedding-based comparison of reaction networks of Wnt signaling. MATCH Communications in Mathematical and in Computer Chemistry, 93(1), 235-245. https://doi.org/10.46793/match.93-1.223h

Habang patuloy ang paghina o pagkawalang bisa ng mga tradisyunal na antibiotic sanhi ng antimicrobial resistance (AMR), ay lalong nagiging mahalaga ang paghahanap ng mga bagong gamot. Isa sa mga pinakapromising na solusyon ay ang antimicrobial peptides (AMPs), na isang klase ng mga compound na kayang pumatay ng bacteria. Upang mapabilis ang proseso ng pagtuklas ng AMPs, bumuo sila Remmer Salas, Dr. Portia Mahal Sabido, at Dr. Ricky Nellas ng University of the Philippines Diliman – College of Science (UPD-CS) ng isang AI tool.

Ang ISCAPE (Interpretable Support Vector Classifier of Antibacterial Activity of Peptides against Escherichia coli) ay isang AI-powered tool na tumutulong na mag-predict kung ang isang peptide ay may kakayahang pumatay o pumigil sa pagdami ng E. coli. Kailangan lamang ng system na ito ng Simplified Molecular-Input Line-Entry System (SMILES) string bilang input, bagay na magpapadali sa mga researcher na suriin ang mga kandidatong molecule.

“Ayon sa tradisyunal na paraan, ang pagtuklas ng antibacterial peptides ay nangangailangan ng paggawa o pag-synthesize ng maraming kandidato at pagsubok sa mga ito isa-isa. Matagal ang prosesong ito,” sabi ni Salas. “Gumamit kami ng AI upang matuto mula sa umiiral na datos at matukoy ang mga pattern na nagkakaiba sa mga aktibo at hindi aktibong peptide.”

Bukod-tangi din sa mga AI tools ang abilidad ng ISCAPE na tukuyin kung aling mga molecular feature ang nagpapabisa sa isang peptide. Ayon kay Salas, nakatutulong ito upang makatipid ng oras at resources ang mga researcher. Nababawasan nito ang trial-and-error experiments at nagbibigay-daan upang mas mahusay na makapagdisenyo ng peptide.

“Tumutulong ang ISCAPE sa laban sa antimicrobial resistance sa pamamagitan ng pagpapabilis ng early-stage screening gamit ang data-driven peptide design,” dagdag pa ni Salas. “Hindi nito pinapalitan ang laboratory experiments, ngunit ginagawa nitong mas episyente ang discovery process at tinutulungan ang mga researcher na magpokus sa mga pinakapromising na kandidato.”

Maaaring ding magamit ang AI-powered model  laban sa ibang bacteria bukod sa E. coli. Gayunpaman, kailangan muna itong sanayin muli gamit ang mataas na kalidad na datasets na partikular sa bacterial strains na nais pag-aralan. Maaari ring gamitin ang ISCAPE approach upang hulaan o mag-predict ng activity ng iba pang bioactive peptides.

“Ang paggamit ng ISCAPE para sa ibang biological targets ay nangangailangan ng maayos na curated datasets na may experimentally validated activity,” dagdag ni Salas. “Pagkatapos nito, kailangang sanayin muli ang model gamit ang molecular features na natukoy naming pinakamainam para sa peptides.”

Umaasa ang grupo na makatutulong ang ISCAPE upang mas mahusay at mas mabilis na makapagdisenyo ang mga researcher ng antibacterial peptides at makatulong sa pandaigdigang laban kontra AMR.

Bukas sa publiko ang ISCAPE para magamit ng ibang siyentista. Maaaring ma-access ang web server nito sa Hugging Face Spaces. Available din sa GitHub ang training data at code para sa large-scale prediction.

Ang research paper na pinamagatang “Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli” ay nailathala sa Journal of Molecular Graphics and Modelling. Ang internasyonal na publikasyong ito ay naglalaman ng mga papel tungkol sa paggamit ng computer sa teoretikal na pag-aaral ng molecular structure, function, interaction, at design. Saklaw nito ang lahat ng aspeto ng molecular modeling at computational chemistry.

References:

Salas, R. L., Sabido, P. M., & Nellas, R. B. (2026). Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli. Journal of Molecular Graphics and Modelling, 142, 109188. https://doi.org/10.1016/j.jmgm.2025.109188

As antimicrobial resistance (AMR) continues to make traditional antibiotics less effective worldwide, the search for new antibacterial treatments has become increasingly urgent. One promising solution is antimicrobial peptides (AMPs), small molecules that can kill bacteria. To help accelerate their discovery, researchers at the University of the Philippines Diliman – College of Science (UPD-CS) have developed an AI tool designed to accelerate the discovery of new antibacterial peptides.

Remmer Salas, Dr. Portia Mahal Sabido, and Dr. Ricky Nellas of the UPD-CS Institute of Chemistry (IC) developed ISCAPE (Interpretable Support Vector Classifier of Antibacterial Activity of Peptides against Escherichia coli). It is an AI-powered tool that helps experimentalists predict whether a peptide can kill or inhibit the growth of E. coli. The system requires only a Simplified Molecular-Input Line-Entry System (SMILES) string as input, making it simple for researchers to evaluate candidate molecules.

“Traditionally, discovering antibacterial peptides means synthesizing many candidates and testing them one by one. This process is time-consuming,” said Salas. “We used AI to learn from existing data and identify patterns that distinguish active peptides from inactive ones.”

Unlike many AI tools, ISCAPE also shows which molecular features make a peptide effective. He noted that this helps researchers save time and resources. It reduces trial-and-error experiments and allows them to design better peptides more efficiently.

“ISCAPE helps address antimicrobial resistance by accelerating early-stage screening through data-driven peptide design,” Salas further explained. “It doesn’t replace laboratory experiments, but it makes discovery more efficient and helps researchers focus on the most promising candidates.”

The AI-powered model can be adapted to predict activity against bacteria other than E. coli. However, it would need to be retrained using high-quality datasets specific to the bacterial strains of interest. The ISCAPE approach could also be applied to predict the activity of other bioactive peptides. 

“Applying ISCAPE to other biological targets requires well-curated datasets with experimentally validated activity,” Salas added. “The model must then be retrained using the molecular features we identified as optimal for peptides.”

The team hopes the tool will help researchers design better antibacterial peptides more efficiently and contribute to the global fight against AMR.

ISCAPE is publicly available for other scientists. The web server can be accessed through Hugging Face Spaces. The training data and code for large-scale prediction are available on GitHub.

The chemists’ research paper, titled “Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli,” was included in the Journal of Molecular Graphics and Modelling. This international publication features papers on the use of computers in theoretical investigations of molecular structure, function, interaction, and design. It covers all aspects of molecular modeling and computational chemistry.

References:

Salas, R. L., Sabido, P. M., & Nellas, R. B. (2026). Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli. Journal of Molecular Graphics and Modelling, 142, 109188. https://doi.org/10.1016/j.jmgm.2025.109188

Sa mga tropikal na ilog tulad ng mga matatagpuan sa Pilipinas, kadalasan ay naiipon ang mga sediment at nagbabago ng kapaligiran, kaya’t mahalagang magsagawa ng baseline studies sa mga partikular na lugar. Ito ang motibasyon ni Dr. Enrico Replan ng University of the Philippines – Diliman College of Science’s Institute of Environmental Science and Meteorology (UPD-CS IESM) para pag aralan ang mga halamang tumutubo sa tabi ng Amnay River sa Occidental Mindoro at makita kung ano ang ugnayan ng kanilang distribusyon sa kapaligiran ng ilog.

Malawak ang sakop ng pag-aaral ni Dr. Replan- mahigit na 14 na kilometro ng downstream river corridor, na sumasaklaw sa mahigit na 509 na ektarya, kasama ang mga pampang ng ilog at kalapit na halamanan na minsan ay umaabot papaloob (inland) sa ibang lugar. Dahil dito, maaring makita ni Dr. Replan kung paano nagbabago ang mga halamanan sa mga lugar na madaling mabaha hanggang sa mga mas terrestrial parts ng riparian landscapes.

“Ang mga riparian areas o mga lugar sa tabi ng ilog, ay napakahalaga dahil sa kakayanan nilang makapigil ng erosyon ng lupa, magpabuti ng kalidad ng tubig, at makapagbigay ng tirahan para sa mga hayop. Sa pagdokumento o pagtala ng mga halaman naroon, nakabuo tayo ng baseline record na maaring magamit sa pag-monitor ng mga pagbabago sa kapaligiran sa hinaharap,” aniya ni Dr. Replan.

Kaunti at limitado ang siyentipikong dokumentasyon ng riparian vegetation ng Amnay River, sa kabila ng kahalagahan nito sa lokal na komunidad. “ Sa harap ng tumitinding pressure sa kapaligiran tulad ng land-use change at sedimentasyon, naramdaman kong importante na magtatag ng baseline ecological data,” dagdag niya, pagbibigay-diin na mahalaga ang pag-unawa sa mga halamanan sa tabi ng ilog para sa konserbasyon at sustainable management.

In tropical rivers such as those in the Philippines, sediment often builds up and landscapes are frequently altered, highlighting the need for site-specific baseline studies. This motivated Dr. Enrico Replan of the University of the Philippines – Diliman College of Science’s Institute of Environmental Science and Meteorology (UPD-CS IESM) to examine the plants growing along the banks of the Amnay River in Occidental Mindoro and how their distribution relates to the river environment.

The study covers a 14-kilometer stretch of the downstream river corridor—spanning about 509 hectares, including riverbanks and nearby vegetation extending inland in some areas—allowing Dr. Replan to capture how plant communities change across flood-prone and more terrestrial parts of the riparian landscape.

“Riparian areas—those found along rivers—are very important because they help prevent soil erosion, improve water quality, and provide habitat for wildlife. By documenting the plants present, the study provided/created a baseline record that can help monitor environmental changes in the future,” he said.

There has been limited scientific documentation of the Amnay River’s riparian vegetation, despite its ecological significance to the local community. “Given increasing environmental pressures such as land-use change and sedimentation, I felt it was important to establish baseline ecological data,” he added, emphasizing that understanding the river’s plant communities is crucial for conservation and sustainable management.