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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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CoDrug: Un marco de cribado molecular virtual y optimización multipropiedad impulsado por texto a través de un modelo

Rui Gu1, Yingxu Liu1, Bingxing Zhu1

  • 1School of Science, China Pharmaceutical University, Nanjing 210009, China.

Journal of chemical information and modeling
|January 22, 2026
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Resumen
Este resumen es generado por máquina.

CoDrug es un nuevo marco de IA para el descubrimiento de fármacos que utiliza texto y estructuras moleculares. Acelera el cribado virtual y la optimización molecular, especialmente cuando los datos 3D son limitados.

Palabras clave:
descubrimiento de fármacosinteligencia artificialaprendizaje automáticocribado virtualoptimización molecularmodelos de lenguaje grandesquímica computacionalbioinformática

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Área de la Ciencia:

  • Química computacional
  • Inteligencia artificial en el descubrimiento de fármacos
  • Bioinformática

Sus antecedentes:

  • El cribado molecular tradicional es costoso computacionalmente y depende de estructuras de proteínas 3D completas.
  • La falta de estructuras 3D fiables dificulta el diseño de fármacos.
  • Los métodos existentes enfrentan limitaciones en velocidad y accesibilidad.

Objetivo del estudio:

  • Presentar CoDrug, un marco de fusión multimodal para el descubrimiento de fármacos asistido por IA.
  • Superar las limitaciones de los métodos tradicionales integrando datos de texto y estructura.
  • Permitir el cribado virtual y la optimización molecular impulsados por texto.

Principales métodos:

  • CoDrug utiliza fusión multimodal combinando datos de texto (SciBERT, ChemFormer) y estructurales (ESM, ChemFormer).
  • El aprendizaje contrastivo alinea las incrustaciones textuales y estructurales en un espacio latente compartido.
  • Dos estrategias de fusión: secuencia texto-proteína y estructura compuesto-texto.

Principales resultados:

  • CoDrug logra un rendimiento competitivo o superior en comparación con las bases de referencia de última generación.
  • El marco se destaca en entornos de cero disparos y cuando los datos estructurales 3D son incompletos.
  • Demuestra una fuerte generalización y expresividad de la representación.

Conclusiones:

  • CoDrug ofrece un paradigma escalable y flexible para el descubrimiento de fármacos en etapa temprana.
  • La interfaz de lenguaje natural reduce la barrera técnica para los químicos.
  • Acelera la integración de hipótesis impulsadas por el lenguaje con el diseño molecular guiado por la estructura.