To　reveal　the　potential　of　physics-informed　neural　networks　in　biochemistry,　a　new　parameter　estimation　method　based　on　modern　physics-informed　machine　learning　tools　was　investigated　and　its　function　was　demonstrated　through　a　case　study　of　enzymatic　synthesis　process　and　the　effects　of　soft　and　hard　boundary　constraint　settings　were　compared　on　the　computational　results.　The　experimental　results　show　that　both　physics-informed　neural　networks　with　soft　and　hard　constraints　can　accurately　estimate　model　parameters,　with　goodness　of　fit　R-　above　0.　98　on　all　observable　variables.　The　resulting　system　model　can　better　reflect　the　dynamic　process　of　the　system.　The　proposed　method　combines　the　advantages　of　model-driven　and　data-driven　approaches　and　achieves　robust　training　results　on　a　small　dataset　based　on　40　noisy　samples,　significantly　reducing　the　required　data.　©　2024　Editorial　Office　of　Chemical　Engineering　(China).　All　rights　reserved.