molecular property prediction

A stain is found at a crime scene. The expert takes a swab, and a few hours later the instrument outputs a list of proteins that made up the fluid. Previously, a rapid test could be wrong if multiple fluids were present: blood mixed with saliva, and the strip gave a false result.

A cartographer who draws not mountains and rivers but invisible bonds between electrons: that's one way to describe the new neural network. Instead of atomic coordinates, it takes molecular orbitals and draws a graph where nodes are orbitals and edges are measures of their quantum entanglement.

A protein in a cell doesn't hang motionless like a mannequin in a store window. Its atoms are constantly jiggling, stretching, and bending, like a plucked guitar string that keeps vibrating after you strum it. To understand how a protein works, you need to know how tightly each part holds onto its neighbors.

A protein in a cell doesn't hang motionless like a mannequin in a store window. Its atoms are constantly jiggling, stretching, and bending, like a plucked guitar string that keeps vibrating after you strum it. To understand how a protein works, you need to know how tightly each part holds onto its neighbors.

A protein engineer is like a chef trying to make a dish that is tasty, healthy, and cheap all at once. Change one ingredient, and you risk ruining another. In the past, computer assistants could only evaluate protein properties one at a time, missing how they are connected.

An interpreter gets distracted by every rustle in the hall, and a one-hour meeting stretches into two. Similarly, molecular foundation models learn the language of molecules from vast corpora, but each new conversation requires fine-tuning. Monroe changes the rules: it doesn't learn a new language but instantly finds common meaning from a few examples.

A protein engineer is like a chef trying to make a dish that is tasty, healthy, and cheap all at once. Change one ingredient, and you risk ruining another. In the past, computer assistants could only evaluate protein properties one at a time, missing how they are connected.

Hunting for a new material is like cooking without a recipe: you try, try again, and often throw things out. Previously, scientists manually flipped through papers and tested compositions one by one. Now there's a "robot chef" that reads the literature itself, mixes components, and checks what came out.

The instrument shines an infrared beam at a sample, and molecules respond with their own absorption pattern, like bells of different tones. This pattern, the spectrum, holds information about the chemical composition. But to read it, a specialist usually consults a library of reference records, which is slow and not always possible.

The instrument shines an infrared beam at a sample, and molecules respond with their own absorption pattern, like bells of different tones. This pattern, the spectrum, holds information about the chemical composition. But to read it, a specialist usually consults a library of reference records, which is slow and not always possible.

In a pot of water, microscopic spheres, colloidal particles, are floating. Each sphere is coated with long polymer chains that look like a brush and carries an electric charge. If you get the temperature and solution composition right, the spheres start arranging themselves into ordered patterns — crystals.

A conventional computer computes by shuttling electric charges along wires. But can you compute with heat? A network of pipes carrying hot and cold water can perform simple logic operations if the pipes are connected in the right order.

When sugar goes into tea, it dissolves. But why doesn't it just fall back out? It's all about chemical potential — a quantity that shows how 'favorable' it is for a substance to be in a given place and state. For a pure substance, it's easy to calculate, but for a mixture of several components, it's a real puzzle.

When sugar goes into tea, it dissolves. But why doesn't it just fall back out? It's all about chemical potential — a quantity that shows how 'favorable' it is for a substance to be in a given place and state. For a pure substance, it's easy to calculate, but for a mixture of several components, it's a real puzzle.

A cookbook without a shopping list: that's what a scientist faces when looking at a scheme of chemical reactions. The arrows and formulas on paper look neat, but once you start hunting in the lab for molecules that will actually behave that way, a real quest begins.

A cookbook without a shopping list: that's what a scientist faces when looking at a scheme of chemical reactions. The arrows and formulas on paper look neat, but once you start hunting in the lab for molecules that will actually behave that way, a real quest begins.

Inside every cell, things are constantly meeting and parting. Some pairs hold tight, like Velcro, while others barely touch, like pollen on the wind. These fleeting touches, where the binding strength is comparable to a grain of salt dissolving in a glass of water, govern many of the cell's signals and decisions.

Predicting the yield of a chemical reaction is a bit like trying to guess the harvest knowing only the soil composition. Traditionally, chemists have relied on long columns of numbers, quantum descriptors, that describe electron energies but say nothing about molecular shape.

Predicting the yield of a chemical reaction is a bit like trying to guess the harvest knowing only the soil composition. Traditionally, chemists have relied on long columns of numbers, quantum descriptors, that describe electron energies but say nothing about molecular shape.

A polar molecule is like a tiny dumbbell with opposite charges at its ends. Normally, such particles quickly stick together or fly apart when they meet — electric forces are too strong. But the authors of the work found a way to turn this aggressiveness into a controlled game.

A protein in a cell is not a rigid part but rather a soft glove that can grasp different objects. Until now, protein designers worked like sculptors, crafting a key for a single lock: one target, one shape. But life is more complex: sometimes a protein needs to recognize two different viruses at once or change its contours depending on its neighbors.

Inside every cell, logistics is in full swing: tiny protein motors, like couriers, haul cargo along a network of microtubules. One of the main workhorses is kinesin, which steps using the energy of ATP (the cell's main energy currency). For a long time, researchers tried to evaluate it like a car: how much fuel it burns, how fast it gets there.

Biologists are increasingly trusting computers to read the "user manuals" of living cells. Six recent AI models (Nicheformer, CellPLM, scGPT-spatial, GenePT, scELMo, and Novae) are like six translators working from different dialects of the same cellular language.

The Sun continuously scribbles magnetic fields across its surface. These patterns are not mere ripples but a handwriting in which nature has encoded warnings of flares. A flare is an explosion that releases the energy of billions of nuclear bombs in minutes, ejecting streams of plasma and radiation into space.

Imagine a vast library where every book is a precise instruction manual for assembling one protein molecule. AlphaFold2 read millions of such books and learned to instantly produce a ready-made 3D model. But inside this network, in its very 'brain,' something more remained — not just assembly rules, but an entire atlas of possible protein motions, its hidden life.

An AI predicting room acoustics is like a student taking an exam with a cheat sheet. If the test points are the same as the training ones, the model simply memorizes the acoustic 'fingerprint' of each position — like a student recognizing a question by the dog-eared corner.

A search party combs a foggy field. At first, people spread out wide: a high 'temperature' of search means a great variety of attempts. Upon finding a trace, they close in. But the speed of convergence is critical — narrow the circle too early, and the best find stays aside. The coordinator assesses the spread of searchers and commands when to speed up or slow down.

In boiling soup, molecules collide chaotically and react. But if you measure how their concentrations change, patterns emerge. This is how carbon chemical networks work — mixtures of molecules that spontaneously 'cook' information, turning each reaction into an elementary computation. Previously they were used only for modeling nature.

In a soup pot, you only need two thermometers to gauge the temperature everywhere — if you know heat travels in circles from the bottom. Science works the same way: from a handful of measurements, you want to reconstruct the full heat picture. The system captures the gist of heat flow, compressing it into a handful of governing numbers.

A driver on a slippery road doesn't know exactly how the car will respond to the steering wheel, and learns on the go, leaving room for error. A smart control algorithm behaves in a similar way.

When a giant star collapses into a supernova or two neutron spheres merge in a cosmic dance, trillions of neutrinos carry away the cataclysm's energy.

Cooking a complex dish requires a clear recipe: first chop, then sauté, add spices at just the right moment. Chemical transformations also follow step-by-step recipes — mechanisms where atoms of carbon and hydrogen rearrange in a strict order. Previously, neural networks often invented nonexistent steps, like a whimsical cook swapping salt for sugar.

Predicting the properties of a new molecule is like guessing the flavor of an unfamiliar dish. The program works like an experienced chef: it “tastes” similar molecules whose properties are already known, and based on their “flavor,” it refines its guess.

Complex machinery, from drones to surgical robots, sometimes behaves like a wild mustang: full of disorder and whims, where the slightest touch triggers a storm. Instead of rote memorization of formulas, the new method observes the system like a horse breaker.

An experienced detective doesn't calculate a criminal's path using the laws of physics: he checks a file of similar cases. The new prediction method works the same way: instead of complex equations, it has a vast archive of trajectories that have already happened — planetary motions, currency exchange rates, signals from space.

An electron microscope is a giant pipe organ: instead of pipes (electromagnetic lenses, instead of air) a stream of accelerated electrons. It once merely painted mute frescoes of the atomic world, condemning scientists to grueling decipherment.

Look into the night sky — before you is not a scattering of lights, but a multi-layered manuscript. Under the bright ink of stars and galaxies appears a faded, almost erased text.

You get a report: one 'letter' in your gene has been swapped. But what does that change? A protein isn't a flat string: it's a dense tangle, where each loop sits in its own pocket. A change deep inside that tangle is like a crack in the foundation, while one on the surface is like a scratch on the facade.

Inside each of us, invisible clocks are ticking. They control the release of cortisol — the hormone that helps us wake up in the morning, cope with anxiety, and keep our immune system on its toes. In adrenal insufficiency, these clocks break down: the body can't produce the right amount of cortisol on its own.

Hybrid perovskites are the rising stars of photovoltaics: their efficiency is growing so fast it feels like we're on the brink of a solar revolution. But these materials, like intricate atomic mosaics, are extremely vulnerable — moisture, heat, and light quickly turn them to dust.