Motif2Mol: Prediction of New Active Compounds Based on Sequence Motifs of Ligand Binding Sites in Proteins Using a Biochemical Language Model

Yoshimori, Atsushi; Bajorath, Jürgen

doi:10.3390/biom13050833

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…In both of these studies, conventional protein–ligand docking scores were used to guide compound prioritization. In a different investigation, a transformer was derived to associate extended sequence motifs of ligand binding sites with active compounds [ 25 ]. In this case, the ability of the model to exactly reproduce ATP site-directed inhibitors of different kinases not included in model training was used as a proof-of-concept criterion (instead of hypothetical scoring).…”

Section: Introductionmentioning

confidence: 99%

“…Potential applications of such models include target validation or compound repurposing. Furthermore, in recent studies, transformer-based language models have been employed to learn mappings of protein sequences to compounds [22][23][24][25]. In the following, models using protein sequence data as input are termed protein language models (PLMs), regardless of the nature of the output sequences.…”

Section: Introductionmentioning

confidence: 99%

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Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Chen,

Bajorath

2024

J Cheminform

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Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated “biochemical” language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications. Scientific contribution The approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Chen,

Bajorath

2024

J Cheminform

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Chemical language models for molecular design

Bajorath

2023

Molecular Informatics

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In drug discovery, chemical language models (CLMs) originating from natural language processing offer new opportunities for molecular design. CLMs have been developed using recurrent neural network (RNN) or transformer architectures. For the predictive performance of RNN‐based encoder‐decoder frameworks and transformers, attention mechanisms play a central role. Among others, emerging application areas for CLMs include constrained generative modeling and the prediction of chemical reactions or drug‐target interactions. Since CLMs are applicable to any compound or target data that can be presented in a sequential format and tokenized, mappings of different types of sequences can be learned. For example, active compounds can be predicted from protein sequence motifs. Novel off‐the‐beat‐path applications can also be considered. For example, analogue series from medicinal chemistry can be perceived and represented as chemical sequences and extended with new compounds using CLMs. Herein, methodological features of CLMs and different applications are discussed.

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Uncovering the Profile of Ubiquitination Motif in Catalytic Proteins using Sequence Contextual Features

Zhang,

Ai,

Fan

et al. 2024

Animals and Zoonoses

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Motif2Mol: Prediction of New Active Compounds Based on Sequence Motifs of Ligand Binding Sites in Proteins Using a Biochemical Language Model

Cited by 7 publications

References 37 publications

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Chemical language models for molecular design

Uncovering the Profile of Ubiquitination Motif in Catalytic Proteins using Sequence Contextual Features

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