| 000 | 01314 am a22002053u 4500 | ||
|---|---|---|---|
| 042 | _adc | ||
| 100 | 1 | 0 |
_aBorkakoti, Neera _eauthor _92812 |
| 700 | 1 | 0 |
_aThornton, Janet M. _eauthor _92813 |
| 245 | 0 | 0 | _aAlphafold2 protein structure prediction : Implications for drug discovery |
| 260 | _c2023-01-06. | ||
| 500 | _a/pmc/articles/PMC7614146/ | ||
| 500 | _a/pubmed/36621153 | ||
| 520 | _aThe drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three dimensional structure of proteins has made protein targets more accessible to the drug design process. Here we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines. | ||
| 540 | _a | ||
| 546 | _aen | ||
| 690 | _aArticle | ||
| 655 | 7 |
_aText _2local |
|
| 786 | 0 | _nCurr Opin Struct Biol | |
| 856 | 4 | 1 |
_uhttp://dx.doi.org/10.1016/j.sbi.2022.102526 _zConnect to this object online. |
| 999 |
_c2264 _d2264 |
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