Biology subjects

Ziv, Y.

Publications and source records attributed to Ziv, Y..

7 recordsLinked to original sources

bioRxiv2025

INTERPOLATION-BASED CONDITIONING OF FLOW MATCHING MODELS FOR BIOISOSTERIC LIGAND DESIGN

AO_SCPLOWBSTRACTC_SCPLOWFast, unconditional 3D generative models can now produce high-quality molecules, but adapting them for specific design tasks often requires costly retraining. To address this, we introduce two training-free, inference-time conditioning strategies, Interpolate-Integrate and Replacement Guidance, that provide control over E(3)-equivariant flow-matching models. Our methods generate bioisosteric 3D molecules by conditioning on seed ligands or fragment sets to preserve key determinants like shape and pharmacophore patterns, without requiring the original fragment atoms to be present. We demonstrate their effectiveness on three drug-relevant tasks: natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging.

molecular biology↗

bioRxiv2024

MolSnapper: Conditioning Diffusion for Structure Based Drug Design

Generative models have emerged as potentially powerful methods for molecular design, yet challenges persist in generating molecules that effectively bind to the intended target. The ability to control the design process and incorporate prior knowledge would be highly beneficial for better tailoring molecules to fit specific binding sites. In this paper, we introduce MolSnapper, a novel tool that is able to condition diffusion models for structure-based drug design by seamlessly integrating expert knowledge in the form of 3D pharmacophores. We demonstrate through comprehensive testing on both the CrossDocked and Binding MOAD datasets, that our method generates molecules better tailored to fit a given binding site, achieving high structural and chemical similarity to the original molecules. It also, when compared to alternative methods, yields approximately twice as many valid molecules.

bioinformatics↗

bioRxiv2023

Differential encoding of odor and place in mouse piriform and entorhinal cortex

The integration of olfactory and spatial information is critical for guiding animal behavior. The lateral entorhinal cortex (LEC) is reciprocally interconnected with cortical areas for olfaction and the hippocampus and thus ideally positioned to encode odor-place associations. Here, we used mini-endoscopes to record neural activity in the mouse piriform cortex (PCx) and LEC. We show that in head-fixed mice, odor identity could be decoded from LEC ensembles, but less accurately than from PCx. In mice freely navigating a linear track, LEC ensemble activity at the odor ports was dominated by spatial information. Spatial position along the linear track could be decoded from LEC and PCx activity, however, PCx but not LEC exhibited strong behavior-driven modulation of positional information. Together, our data reveal that information about odor cues and spatial context is differentially encoded along the PCx-LEC axis. Significance statementFor most animals, the sense of smell is essential for successfully navigating the environment to find food, shelter, and mates. However, how olfactory and spatial information is integrated in the brain to support olfactory-guided behaviors remains poorly understood. In mammals, candidate brain regions thought to support odor-place associations include the olfactory (piriform, PCx) cortex, entorhinal cortex, and hippocampus. We here characterize the activity of cells in the PCx and lateral entorhinal cortex (LEC) of freely moving mice in response to odor cues presented in a linear track. Using mini-endoscope microscopy and population coding analyses, we find that information about odors, spatial location, and behavior is differentially encoded along the PCx-LEC axis.

neuroscience↗

bioRxiv2021

Hippocampal Astrocytes Encode Reward Location

Astrocytic calcium dynamics have been implicated in the encoding of sensory information, and modulating them has been shown to impact behavior. However, real-time calcium activity of astrocytes in the hippocampus of awake mice has never been investigated. We used 2-photon microscopy to chronically image CA1 astrocytes as mice ran in familiar or novel virtual environments and obtained water rewards. We found that astrocytes exhibit persistent ramping activity towards the reward location in a familiar environment, but not in a novel one. Using linear decoders, we could precisely predict the location of the mouse in a familiar environment from astrocyte activity alone. We could not do the same in the novel environment, suggesting astrocyte spatial activity is experience dependent. This is the first indication that astrocytes can encode location in spatial contexts, thereby extending their known computational capabilities, and their role in cognitive functions.

neuroscience↗

bioRxiv2021

Consistent population activity on the scale of minutes in the mouse hippocampus

Neurons in the hippocampus fire in consistent sequence over the timescale of seconds during the delay period of some memory experiments. For longer timescales, firing of hippocampal neurons also changes slowly over minutes within experimental sessions. It was thought that these slow dynamics are caused by stochastic drift or a continuous change in the representation of the episode, rather than consistent sequences unfolding over minutes. This paper studies the consistency of contextual drift in three chronic calcium imaging recordings from the hippocampus CA1 region in mice. Computational measures of consistency show reliable sequences within experimental trials at the scale of seconds as one would expect from time cells or place cells during the trial, as well as across experimental trials on the scale of minutes within a recording session. Consistent sequences in the hippocampus are observed over a wide range of time scales, from seconds to minutes. Hippocampal activity could reflect a scale-invariant spatiotemporal context as suggested by theories of memory from cognitive psychology.

neuroscience↗

bioRxiv2020

Representational drift in the mouse visual cortex

Neuronal representations in the hippocampus and related structures gradually change over time despite no changes in the environment or behavior. The extent to which such representational drift occurs in sensory cortical areas and whether the hierarchy of information flow across areas affects neural-code stability have remained elusive. Here, we address these questions by analyzing large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. We found representational drift over timescales spanning minutes to days across multiple visual areas. The drift was driven mostly by changes in individual cells activity rates, while their tuning changed to a lesser extent. Despite these changes, the structure of relationships between the population activity patterns remained stable and stereotypic, allowing robust maintenance of information over time. Such population-level organization may underlie stable visual perception in the face of continuous changes in neuronal responses.

neuroscience↗

bioRxiv2019

Revealing neural correlates of behavior without behavioral measurements

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a-priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition, and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an internal tuning-curve that characterizes its activity relative to the network activity, rather than relative to any pre-defined external variable - revealing place-tuning in the hippocampus and head-direction tuning in the thalamus and postsubiculum, without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the trajectory-phase. The structure of ensemble activity patterns was conserved across mice, allowing using one animals data to decode another animals behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

neuroscience↗